## Scientific production of the lab

Publications list of the D&C Laboratory

Please send an email to Prof. Mariagrazia Dotoli () if you are interested in getting the preprints of any publication by the Decision and Control Laboratory**–> Research activities and Master/PhD theses of the D&C Lab–> Research metrics of the D&C Lab**

# Research areas

The staff of the laboratory provides research services and technology transfer in the following macro-areas:

### Management and control of complex systems

- Advanced algorithms and ICT applications to predict and solve upcoming situations in various applicative contexts (transport, production, health systems) with minimal or reduced human involvement.
- Decentralized and distributed control and optimization for large scale systems
- Decision Support Systems for planning and management of Intelligent transportation systems, road and railroad traffic,dangerous freight transport.
- Management and optimization of electrical mobility,
- Decision Support Systems for the planning and management of Smart Cities and smart buildings;
- Modelling and management of healthcare systems.
- Modeling, simulation, and control of container terminals and mono and multi-modal logistic systems.

### Modelling, control and optimization for industrial applications

- Re-engineering and automation of manufacturing processes and systems.
- Coordination of agents and sensors networks.
- Fault detection and recovery.
- Problems regarding the logistics area, production and distribution.
- Scheduling and planning problems, workflow management.
- Models for maximizing the effectiveness of technological products and processes.
- Methods for the reduction of alternatives, particularly in case of high quantity of choices.

### Management and control of energy systems

- Strategic level: analysis and decision tools to support the urban policy maker in determining the optimal action plans for long-term energy efficiency in the following areas: building and building grids sectors, public street lighting sector, integrated management of urban energy systems.
- Operational level: solutions for the control and scheduling of the energy activities of smart energy users, optimal planning of the energy activities of a smart home, networks of smart homes, management of the optimal charging of electric vehicles.

## Publications

The scientific production of the lab is available at: https://www.scopus.com/authid/detail.uri?authorId=6603204493

### 2021

- Cavone, G., Carli, R., Troccoli, G., Tresca, G. & Dotoli, M. (2021) A MILP approach for the multi-drop container loading problem resolution in logistics 4.0 IN 2021 29th Mediterranean Conference on Control and Automation, MED 2021., 687-692.

[Bibtex]`@CONFERENCE{Cavone2021687, author={Cavone, G. and Carli, R. and Troccoli, G. and Tresca, G. and Dotoli, M.}, title={A MILP approach for the multi-drop container loading problem resolution in logistics 4.0}, journal={2021 29th Mediterranean Conference on Control and Automation, MED 2021}, year={2021}, pages={687-692}, doi={10.1109/MED51440.2021.9480359}, art_number={9480359}, note={cited By 0}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113699292&doi=10.1109%2fMED51440.2021.9480359&partnerID=40&md5=734929101ce82724d3629ddb1e06f4a4}, abstract={This paper addresses the multi-drop container loading problem (CLP), i.e., the problem of packing multiple bins -associated to multiple deliveries to one or more customers- into a finite number of transport units (TUs). Differently from the traditional CLP, the multi-drop CLP has been rarely handled in the literature, while effective algorithms to automatically solve this problem are needed to improve the efficiency and sustainability of internal logistics. To this aim, we propose a novel algorithm that solves a delivery-based mixed integer linear programming formulation of the problem. The algorithm efficiently determines the optimal composition of TUs by minimizing the unused space, while fulfilling a set of geometric and safety constraints, and complying with the delivery allocation. In particular, the proposed algorithm includes two steps: the first aims at clustering bins into groups to be compatibly loaded in various TUs; the latter aims at determining the optimal configuration of each group in the related TU. Finally, the proposed algorithm is applied to several realistic case studies with the aim of testing and analysing its effectiveness in producing stable and compact TU loading configurations in a short computation time, despite the high computational complexity of the multi-drop CLP. © 2021 IEEE.}, author_keywords={Container loading problem; Logistics; MILP; Multi-drop; Optimization}, keywords={Bins; Drops; Integer programming, Container-loading problems; Effective algorithms; Internal Logistics; Loading configuration; Mixed integer linear programming; Multiple deliveries; Optimal composition; Safety constraint, Clustering algorithms}, references={Strandhagen, J.O., Vallandingham, L.R., Fragapane, G., Strandhagen, J.W., Stangeland, A.B.H., Sharma, N., Logistics 4. 0 and emerging sustainable business models (2017) Advances in Manufacturing, 5 (4), pp. 359-369; Digiesi, S., Facchini, F., Mossa, G., Mummolo, G., Minimizing and balancing ergonomic risk of workers of an assembly line by job rotation: A minlp model (2018) International Journal of Industrial Engineering and Management, 9 (3), pp. 129-138; Bischoff, E.E., Ratcliff, M., Issues in the development of approaches to container loading (1995) Omega, 23 (4), pp. 377-390; Facchini, F., Digiesi, S., Mossa, G., Optimal dry port configuration for container terminals: A non-linear model for sustainable decision making (2020) International Journal of Production Economics, 219, pp. 164-178; Wäscher, G., Haußner, H., Schumann, H., An improved typology of cutting and packing problems (2007) European Journal of Operational Research, 183 (3), pp. 1109-1130; Bortfeldt, A., Wäscher, G., Constraints in container loading-a stateof-the-art review (2013) European Journal of Operational Research, 229 (1), pp. 1-20; Scheithauer, G., Terno, J., Riehme, J., Sommerweiss, U., A new heuristic approach for solving the multi-pallet packing problem (1996) Dresden: Technische Universität Dresden; Pisinger, D., Heuristics for the container loading problem (2002) European Journal of Operational Research, 141 (2), pp. 382-392; Moura, A., Oliveira, J.F., A grasp approach to the container-loading problem (2005) IEEE Intelligent Systems, 20 (4), pp. 50-57; Jin, Z., Ohno, K., Du, J., An efficient approach for the threedimensional container packing problem with practical constraints (2004) Asia-Pacific Journal of Operational Research, 21 (3), pp. 279-295; Lin, J.-L., Chang, C.-H., Yang, J.-Y., A study of optimal system for multiple-constraint multiple-container packing problems (2006) International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, pp. 1200-1210. , Springer; Ceschia, S., Schaerf, A., Local search for a multi-drop multicontainer loading problem (2013) Journal of Heuristics, 19 (2), pp. 275-294; Alonso, M., Alvarez-Valdes, R., Iori, M., Parreño, F., Mathematical models for multi container loading problems with practical constraints (2019) Computers & Industrial Engineering, 127, pp. 722-733; Do Nascimento, O.X., De Queiroz, T.A., Junqueira, L., Practical constraints in the container loading problem: Comprehensive formulations and exact algorithm (2021) Computers & Operations Research, 128, p. 105186; Dotoli, M., Epicoco, N., Falagario, M., Seatzu, C., Turchiano, B., A decision support system for optimizing operations at intermodal railroad terminals (2016) IEEE Transactions on Systems, Man, and Cybernetics: Systems, 47 (3), pp. 487-501; Dotoli, M., Epicoco, N., A technique for the optimal management of containers' drayage at intermodal terminals (2016) 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, pp. 000566-000571; Junqueira, L., Morabito, R., Yamashita, D.S., Mip-based approaches for the container loading problem with multi-drop constraints (2012) Annals of Operations Research, 199 (1), pp. 51-75; Lai, K., Xue, J., Xu, B., Container packing in a multi-customer delivering operation (1998) Computers & Industrial Engineering, 35 (1-2), pp. 323-326; Christensen, S.G., Rousøe, D.M., Container loading with multidrop constraints (2009) International Transactions in Operational Research, 16 (6), pp. 727-743; Bemporad, A., Morari, M., Control of systems integrating logic, dynamics, and constraints (1999) Automatica, 35 (3), pp. 407-427}, document_type={Conference Paper}, source={Scopus}, }`

- Helmi, A. M., Carli, R., Dotoli, M. & Ramadan, H. S. (2021) Harris hawks optimization for the efficient reconfiguration of distribution networks IN 2021 29th Mediterranean Conference on Control and Automation, MED 2021., 214-219.

[Bibtex]`@CONFERENCE{Helmi2021214, author={Helmi, A.M. and Carli, R. and Dotoli, M. and Ramadan, H.S.}, title={Harris hawks optimization for the efficient reconfiguration of distribution networks}, journal={2021 29th Mediterranean Conference on Control and Automation, MED 2021}, year={2021}, pages={214-219}, doi={10.1109/MED51440.2021.9480179}, art_number={9480179}, note={cited By 0}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113690851&doi=10.1109%2fMED51440.2021.9480179&partnerID=40&md5=ae29f6caf996f43c23ab7874ded72442}, abstract={Improving the efficiency of distribution networks (DNs) is nowadays a challenging objective for modern power grids equipped with distributed generation and storage. In this context, the so-called network reconfiguration problem can be solved to obtain the optimal DN topology that minimizes the total power losses, while ensuring the voltage profile enhancement. The DN reconfiguration problem has NP-hard complexity; hence, finding near-optimal solutions in reasonable time is still an open research need. Facing this issue, this paper proposes a novel metaheuristic approach, where the recent Harris Hawks optimization algorithm is used to efficiently obtain near-optimal configurations. The effectiveness of the proposed method is validated through numerical experiments on the IEEE 85-bus system, comparing the achieved performance with the results obtained by other related techniques. © 2021 IEEE.}, author_keywords={Distribution Network Reconfiguration; Harris Hawks Optimization; Metaheuristic Optimization; Microgrids}, keywords={Electric power transmission networks; NP-hard; Numerical methods, Distributed generation and storage; Meta-heuristic approach; Near-optimal solutions; Network re-configuration; Optimization algorithms; Reconfiguration of distribution networks; Reconfiguration problems; Voltage profile enhancements, Optimization}, references={Hosseini, S.M., Carli, R., Dotoli, M., Robust optimal energy management of a residential microgrid under uncertainties on demand and renewable power generation (2021) IEEE Trans. Autom. Sci. Eng., 18 (2), pp. 618-637; Scarabaggio, P., Grammatico, S., Carli, R., Dotoli, M., Distributed demand side management with stochastic wind power forecasting (2021) IEEE Trans. Control Syst. Technol; Casalino, G., Castellano, G., Fanelli, A.M., Mencar, C., Enhancing the dissfcm algorithm for data stream classification (2018) International Workshop on Fuzzy Logic and Applications, pp. 109-122. , Springer, Cham September; Piccinni, G., Avitabile, G., Coviello, G., Talarico, C., A novel optimization framework for the design of gilbert cell mixers (2017) IEEE Int. Midwest Symposium on Circuits and Systems (MWSCAS), pp. 1418-1421; Dotoli, M., Epicoco, N., Falagario, M., Cavone, G., A timed petri nets model for intermodal freight transport terminals (2014) IFAC Proceedings Volumes, 47 (2), pp. 176-181; Cavone, G., Dotoli, M., Epicoco, N., Franceschelli, M., Seatzu, C., Hybrid petri nets to re-design low-automated production processes: The case study of a sardinian bakery (2018) IFAC-PapersOnLine, 51 (7), pp. 265-270; Digiesi, S., Facchini, F., Mossa, G., Mummolo, G., Minimizing and balancing ergonomic risk of workers of an assembly line by job rotation: A minlp model (2018) Int. J. Ind. Eng. Manag., 9 (3), pp. 129-138; Babaei, S., Jiang, R., Zhao, C., Distributionally robust distribution network configuration under random contingency (2020) IEEE Trans. Power Syst., 35 (5), pp. 3332-3341; Anderos, A., Koziel, S., Abdel-Fattah, M.F., Distribution network reconfiguration using feasibility-preserving evolutionary optimization (2019) J. Mod. Power Syst. Clean Energy, 7, pp. 589-598; Zhang, D., Fu, Z., Zhang, L., An improved ts algorithm for lossminimum reconfiguration in large-scale distribution systems (2007) ELECTR POW SYST RES, 77 (5-6), pp. 685-694; Samman, M.A., Mokhlis, H., Mansor, N.N., Mohamad, H., Suyono, H., Sapari, N.M., Fast optimal network reconfiguration with guided initialization based on a simplified network approach (2020) IEEE Access, 8, pp. 11948-11963; Mohamed, S., Shaaban, M.F., Ismail, M., Serpedin, E., Qaraqe, K.A., An efficient planning algorithm for hybrid remote microgrids (2019) IEEE Trans. Sustain. Energy, 10 (1), pp. 257-267; Merlin, A., Back, H., Search for a minimal loss operating spanning tree configuration in an urban power distribution system (1975) Proc. 5th Power System Computation Conf (PSCC), pp. 1-18; Civanlar, S., Grainger, J.J., Le, S.S.H., Distribution feeder reconfiguration for loss reduction (1988) IEEE Trans. Power Deliv., 3, pp. 1217-1223; Qiu, R., Lv, X., Chen, S., A survey on artificial intelligence algorithm for distribution network reconfiguration (2011) LECT NOTES CONTR INF, pp. 497-504. , Springer; Badran, O., Mekhilef, S., Mokhlis, H., Dahalan, W., Optimal reconfiguration of distribution system connected with distributed generations: A review of different methodologies (2017) Renewable and Sustainable Energy Reviews, 73, pp. 854-867; Thakar, S., Vijay, A.S., Doolla, S., System reconfiguration in microgrids (2019) Sustainable Energy, Grids and Networks, 17, p. 100191; Jeon, Y., Kim, J., Kim, J., Shin, J., Lee, K.Y., An efficient simulated annealing algorithm for network reconfiguration in large-scale distribution systems (2002) IEEE Trans. Power Del., 17 (4), pp. 1070-1078; Tandon, A., Saxena, D., A comparative analysis of spso and bpso for power loss minimization in distribution system using network reconfiguration (2014) 2014 Innovative Applications of Computational Intelligence on Power, Energy and Controls with Their Impact on Humanity (CIPECH), pp. 226-232; Nguyen, T.T., Truong, A.V., Distribution network reconfiguration for power loss minimization and voltage profile improvement using cuckoo search algorithm (2015) INT J ELEC POWER, 68, pp. 233-242; Muhammad, M.A., Mokhlis, H., Naidu, K., Amin, A., Franco, J.F., Othman, M., Distribution network planning enhancement via network reconfiguration and dg integration using dataset approach and water cycle algorithm (2020) J MOD POWER SYST CLE, 8 (1), pp. 86-93; Prasad, K., Ranjan, R., Sahoo, N.C., Chaturvedi, A., Optimal configuration of radial distribution systems using a fuzzy mutated genetic algorithm (2005) IEEE Trans. Power Del., 20, pp. 1211-1213; Imran Md, A., Kowsalya, M., A new power system reconfiguration scheme for power loss minimization and voltage profile enhancement using fireworks algorithm (2014) Electr Power Syst Res, 62, pp. 312-322; Aman, M.M., Jamson, G.B., Aha, B., Mokhilis, H., Optimum network reconfiguration based on maximization of system load ability using continuation power flow theorem (2014) Electr Power Energy Syst, 54, pp. 123-133; Chen, T.H., Chen, M.S., Hwang, K.J., Kotas, P., Chebli, E.A., Distribution system power flow analysis-a rigid approach (1991) IEEE Trans. On Power Delivery, 6 (3), pp. 1146-1152; Lavorato, M., Franco, J.F., Rider, M.J., Romero, R., Imposing radiality constraints in distribution system optimization problems (2011) IEEE Trans. Power Syst., 27 (1), pp. 172-180; Garces, A., Uniqueness of the power flow solutions in low voltagedirect current grids (2017) Electric Power Systems Research, 151, pp. 149-153; Heidari, A.A., Mirjalili, S., Faris, H., Aljarah, I., Mafarja, M., Chen, H., Harris hawks optimization: Algorithm and applications (2019) Future Generation Computer Systems, 97, pp. 849-872; Yang, X.-S., Ting, T., Karamanoglu, M., Walks, R., Lévy flights, markov chains and metaheuristic optimization (2013) Future Information Communication Technology and Applications, pp. 1055-1064. , Springer; Zimmerman, R.D., Murillo-Sanchez, C.E., Thomas, R.J., Matpower: Steady-state operations, planning and analysis tools for power systems research and education (2011) IEEE Trans Power Syst, 26 (1), pp. 12-19}, document_type={Conference Paper}, source={Scopus}, }`

- Cavone, G., Epicoco, N., Carli, R., Del Zotti, A., Paulo Ribeiro Pereira, J. & Dotoli, M. (2021) Parcel delivery with drones: Multi-criteria analysis of trendy system architectures IN 2021 29th Mediterranean Conference on Control and Automation, MED 2021., 693-698.

[Bibtex]`@CONFERENCE{Cavone2021693, author={Cavone, G. and Epicoco, N. and Carli, R. and Del Zotti, A. and Paulo Ribeiro Pereira, J. and Dotoli, M.}, title={Parcel delivery with drones: Multi-criteria analysis of trendy system architectures}, journal={2021 29th Mediterranean Conference on Control and Automation, MED 2021}, year={2021}, pages={693-698}, doi={10.1109/MED51440.2021.9480332}, art_number={9480332}, note={cited By 0}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113688309&doi=10.1109%2fMED51440.2021.9480332&partnerID=40&md5=b1147e916948e156664d7ff61520c1dd}, abstract={New technologies, such as Unmanned Aerial Vehicles (UAVs), are transforming facilities and vehicles into intelligent systems that will significantly modify logistic deliveries in any organization. With the appearance of automated vehicles, drones offer multiple new technological solutions that might trigger different delivery networks or boost new delivery services. Differently from the related works, where a single specific delivery system model is typically addressed, this paper deals with the use of UAVs for logistic deliveries focusing on a multi-criteria analysis of trendy drone-based system architectures. In particular, using the cross-efficiency Data Envelopment Analysis approach, a comparative analysis among three different delivery systems is performed: the classic system based on trucks only, the drone-only system using a fleet of drones, and the hybrid truck and drone system combining trucks and drones. The proposed technique constitutes an effective decision-making tool aimed at helping delivery companies in selecting the optimal delivery system architecture according to their specific needs. The effectiveness of the proposed methodology is shown by a simulation analysis based on a realistic data case study that pertains to the main logistic service providers. © 2021 IEEE.}, author_keywords={Data Envelopment Analysis; Drones; Multi-criteria decision making; Parcel delivery; UAVs}, keywords={Antennas; Automation; Automobiles; Data envelopment analysis; Decision making; Drones; Fleet operations; Intelligent systems; Network architecture; Trucks, Automated vehicles; Comparative analysis; Decision making tool; Logistic services; Multi Criteria Analysis; Simulation analysis; System architectures; Technological solution, Computer architecture}, references={Facchini, F., Digiesi, S., Mossa, G., Optimal dry port configuration for container terminals: A non-linear model for sustainable decision making (2020) Int. J. Prod. Econ., 219, pp. 164-178; Bhatti, A., Akram, H., Basit, H., Khan, A., Mahwish, S., Naqvi, R., Bilal, M., E-commerce trends during COVID-19 pandemic (2020) Int. J. Future Gener. Commun. Netw., 13; Ranieri, L., Digiesi, S., Silvestri, B., Roccotelli, M., A review of last mile logistics innovations in an externalities cost reduction vision (2018) Sustainability, 10 (782); Amazon Testing Drones for Deliveries, , www.bbc.com, accessed: 2021-01-04; Jung, S., Kim, H., Analysis of amazon prime air UAV delivery service (2017) J. Knowl. Inf. Technol. Syst., 12, pp. 253-266; Guerrero, M.E., Mercado, D., Lozano, R., García, C., Passivity based control for a quadrotor UAV transporting a cable-suspended payload with minimum swing (2015) 54th IEEE Conf. Decision and Control. IEEE, pp. 6718-6723; Peterson, K., Dektas, M., (2017) Ups Tests Residential Delivery Via Drone Launched from Atop Package Car, , www.pressroom.ups.com; Carlsson, J.G., Song, S., Coordinated logistics with a truck and a drone (2018) Manage. Sci., 64 (9), pp. 4052-4069; Lee, T., Geometric control of quadrotor uavs transporting a cablesuspended rigid body (2017) IEEE Trans. Control Syst. Technol., 26 (1), pp. 255-264; Park, S., Zhang, L., Chakraborty, S., Battery assignment and scheduling for drone delivery businesses (2017) IEEE/ACM Int. Symp. Low Power Electronics and Design. IEEE, pp. 1-6; Dorling, K., Heinrichs, J., Messier, G.G., Magierowski, S., Vehicle routing problems for drone delivery (2016) IEEE Trans. Syst. Man Cybern. Syst., 47 (1), pp. 70-85; Gatteschi, V., Lamberti, F., Paravati, G., Sanna, A., Demartini, C., Lisanti, A., Venezia, G., New frontiers of delivery services using drones: A prototype system exploiting a quadcopter for autonomous drug shipments (2015) 39th IEEE Ann. Computer Software and Applications Conf., 2, pp. 920-927; Charnes, A., Cooper, W., Rhodes, E., Measuring the efficiency of decision making units (1978) Eur. J. Oper. Res., 2 (6), pp. 429-444; Dotoli, M., Epicoco, N., Falagario, M., Multi-criteria decision making techniques for the management of public procurement tenders: A case study (2020) Appl. Soft Comput., 88. , 106064; Dotoli, M., Epicoco, N., Falagario, M., Sciancalepore, F., A crossefficiency fuzzy data envelopment analysis technique for performance evaluation of decision making units under uncertainty (2015) Comput. Ind. Eng., 79, pp. 103-114; Sexton, T.R., Silkman, R.H., Hogan, A.J., Data envelopment analysis: Critique and extensions (1986) Measuring Efficiency: An Assessment of Data Envelopment Analysis, pp. 73-105. , R. H. Silkman, Ed. San Francisco, CA: Jossey-Bass; Guo, R., Dong, Y., Meiqiang, W., Yongjun, L., Dea cross-efficiency evaluation method based on good relationship (2015) Int. J. Syst. Sci., 3 (1), pp. 14-24; Jeong, H.Y., Song, B.D., Lee, S., Truck-drone hybrid delivery routing: Payload-energy dependency and no-fly zones (2019) Int. J. Prod. Econ., 214, pp. 220-233; Linnik, I., (2018) How to Implement Drone Delivery Service for An Ecommerce Store, , www.onilab.com; Kesteloo, H., (2018) Drone Delivery Patent Issued to Workhorse for Their Horsefly Truck Launched Drone Package Delivery System, , www.dronedj.com; Murthy, J.G., Harshith, P.S., Joel, J.A., Rakesh, K., Sharath Kumar, A.J., Autonomous drone delivery system: A survey (2020) Int. Res. J. Eng. Technol., 7 (3), pp. 762-766; Ducato, , www.fiatprofessional.com, in Italian, accessed: 2021-01-04; www.planzer.ch, in Italian, accessed: 2021-01-04; Joshi, A., Kale, S., Chandel, S., Pal, D., Likert scale: Explored and explained (2015) Br. J. Appl. Sci. Technol., 7, pp. 396-403; Google Maps, , www.google.it/maps, accessed: 2021-01-04; Orlando, C., (2020) Prezzi Carburanti: Costo Benzina, Diesel, Gpl e Metano. Petrolio in Ribasso, , www.money.it, in Italian; I Prezzi Luce in Italia e all'Estero, , www.sorgenia.it, in Italian, accessed: 2021-01-04; EcoTransIT World, , www.ecotransit.org, accessed: 2021-01-04; Meet the Coolest Robots Working in Energy, , www.equinor.com, accessed: 2021-01-04; Vallecchi, L., (2019) Auto Elettriche e Diesel, un Confronto Su Emissioni di CO2 e Inquinanti, , www.qualenergia.it, in Italian}, document_type={Conference Paper}, source={Scopus}, }`

- Scarabaggio, P., Carli, R., Jantzen, J. & Dotoli, M. (2021) Stochastic model predictive control of community energy storage under high renewable penetration IN 2021 29th Mediterranean Conference on Control and Automation, MED 2021., 973-978.

[Bibtex]`@CONFERENCE{Scarabaggio2021973, author={Scarabaggio, P. and Carli, R. and Jantzen, J. and Dotoli, M.}, title={Stochastic model predictive control of community energy storage under high renewable penetration}, journal={2021 29th Mediterranean Conference on Control and Automation, MED 2021}, year={2021}, pages={973-978}, doi={10.1109/MED51440.2021.9480353}, art_number={9480353}, note={cited By 0}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113644274&doi=10.1109%2fMED51440.2021.9480353&partnerID=40&md5=6156faadbaf382124749771a715a60df}, abstract={This paper focuses on the robust optimal on-line scheduling of a grid-connected energy community, where users are equipped with non-controllable (NCLs) and controllable loads (CLs) and share renewable energy sources (RESs) and a community energy storage system (CESS). Leveraging on the pricing signals gathered from the power grid and the predicted values for local production and demand, the energy activities inside the community are decided by a community energy manager. Differently from literature contributions commonly focused on deterministic optimal control schemes, to cope with the uncertainty that affects the forecast of the inflexible demand profile and the renewable production curve, we propose a Stochastic Model Predictive Control (MPC) approach aimed at minimizing the community energy costs. The effectiveness of the method is validated through numerical experiments on the marina of Ballen, Samso (Denmark). The comparison with a standard deterministic optimal control approach shows that the proposed stochastic MPC achieves higher performance in terms of minimized energy cost and maximized self-consumption of on-site production. © 2021 IEEE.}, author_keywords={Community energy storage; Community renewables; Energy community; Energy management; On-line energy scheduling; Stochastic model predictive control}, keywords={Electric power transmission networks; Energy storage; Model predictive control; Numerical methods; Optimization; Predictive control systems; Renewable energy resources; Stochastic control systems; Stochastic systems, Comparison with a standard; Controllable loads; Local production; Numerical experiments; On-site production; Online scheduling; Optimal control scheme; Renewable energy source, Stochastic models}, references={Gjorgievski, V.Z., Cundeva, S., Georghiou, G.E., Social arrangements, technical designs and impacts of energy communities: A review (2021) Renewable Energy; Walker, G., Devine-Wright, P., Community renewable energy: What should it mean? (2008) Energy Policy, 36 (2), pp. 497-500; Hosseini, S.M., Carli, R., Dotoli, M., Robust optimal energy management of a residential microgrid under uncertainties on demand and renewable power generation (2021) IEEE Transactions on Automation Science and Engineering, 18 (2), pp. 618-637; Bartolini, A., Carducci, F., Muñoz, C.B., Comodi, G., Energy storage and multi energy systems in local energy communities with high renewable energy penetration (2020) Renewable Energy, 159, pp. 595-609; Dotoli, M., Epicoco, N., Falagario, M., Turchiano, B., Cavone, G., Convertini, A., A decision support system for real-time rescheduling of railways (2014) 2014 European Control Conference (ECC). IEEE, pp. 696-701; Boenzi, F., Digiesi, S., Facchini, F., Mossa, G., Mummolo, G., Greening activities in warehouses: Amodel for identifying sustainable strategies in material handling (2015) Annals of DAAAM & Proceedings, 26 (1); Piccinni, G., Avitabile, G., Coviello, G., Talarico, C., A novel optimization framework for the design of gilbert cell mixers (2017) 2017 IEEE 60th International Midwest Symposium on Circuits and Systems (MWSCAS). IEEE, pp. 1418-1421; Hossain, M.A., Pota, H.R., Squartini, S., Zaman, F., Guerrero, J.M., Energy scheduling of community microgrid with battery cost using particle swarm optimisation (2019) Applied Energy, 254, p. 113723; Pippia, T., Sijs, J., De Schutter, B., A single-level rule-based model predictive control approach for energy management of grid-connected microgrids (2019) IEEE Transactions on Control Systems Technology, 28 (6), pp. 2364-2376; Pezeshki, H., Wolfs, P., Ledwich, G., A model predictive approach for community battery energy storage system optimization (2014) IEEE PES General Meeting| Conference & Exposition. IEEE, pp. 1-5; Carli, R., Dotoli, M., Jantzen, J., Kristensen, M., Othman, S.B., Energy scheduling of a smart microgrid with shared photovoltaic panels and storage: The case of the ballen marina in samsø (2020) Energy, 198, p. 117188; Lv, C., Yu, H., Li, P., Wang, C., Xu, X., Li, S., Wu, J., Model predictive control based robust scheduling of community integrated energy system with operational flexibility (2019) Applied Energy, 243, pp. 250-265; Scarabaggio, P., Grammatico, S., Carli, R., Dotoli, M., Distributed demand side management with stochastic wind power forecasting (2021) IEEE Trans. Control Syst. Technol., pp. 1-16; Parisio, A., Rikos, E., Glielmo, L., Stochastic model predictive control for economic/environmental operation management of microgrids: An experimental case study (2016) Journal of Process Control, 43, pp. 24-37; Zhang, Y., Meng, F., Wang, R., Zhu, W., Zeng, X.-J., A stochastic MPC based approach to integrated energy management in microgrids (2018) Sustainable Cities and Society, 41, pp. 349-362; Zhang, Y., Fu, L., Zhu, W., Bao, X., Liu, C., Robust model predictive control for optimal energy management of island microgrids with uncertainties (2018) Energy, 164, pp. 1229-1241; Ramanathan, B., Vittal, V., A framework for evaluation of advanced direct load control with minimum disruption (2008) IEEE Transactions on Power Systems, 23 (4), pp. 1681-1688; Shapiro, A., Stochastic programming approach to optimization under uncertainty (2008) Mathematical Programming, 112 (1), pp. 183-220; Kim, S., Pasupathy, R., Henderson, S.G., A guide to sample average approximation (2015) Handbook of Simulation Optimization, pp. 207-243; Jantzen, J., Requirements specification: Deliverable d3. 4 [internet (2019) SMILE, , https://www.h2020smile.eu; Jantzen, J., Kristensen, M., (2019) The Ballen2016 Data Set, , http://arkiv.energiinstituttet.dk/643/, Accessed on: 20-01-2020}, document_type={Conference Paper}, source={Scopus}, }`

- Scarabaggio, P., Carli, R., Cavone, G., Epicoco, N. & Dotoli, M. (2021) Modeling, estimation, and analysis of COVID-19 secondary waves: The Case of the Italian Country IN 2021 29th Mediterranean Conference on Control and Automation, MED 2021., 794-800.

[Bibtex]`@CONFERENCE{Scarabaggio2021794, author={Scarabaggio, P. and Carli, R. and Cavone, G. and Epicoco, N. and Dotoli, M.}, title={Modeling, estimation, and analysis of COVID-19 secondary waves: The Case of the Italian Country}, journal={2021 29th Mediterranean Conference on Control and Automation, MED 2021}, year={2021}, pages={794-800}, doi={10.1109/MED51440.2021.9480319}, art_number={9480319}, note={cited By 0}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113602900&doi=10.1109%2fMED51440.2021.9480319&partnerID=40&md5=980feaa724975719c46c214ad1dcbfed}, abstract={The recent trends of the COVID-19 research have been devoted to disease transmission modeling, with the aim of investigating the effects of different mitigation strategies mainly through scenario-based simulations. In this context we propose a novel non-linear time-varying model that effectively supports policy-makers in predicting and analyzing the dynamics of COVID-19 secondary waves. Specifically, this paper proposes an accurate SIRUCQTHE epidemiological model to get reliable predictions on the pandemic dynamics. Differently from the related literature, in the fitting phase, we make use of the google mobility reports to identify and predict the evolution of the infection rate. The effectiveness of the presented method is tested on the network of Italian regions. First, we describe the Italian epidemiological scenario in the COVID-19 second wave of contagions, showing the raw data available for the Italian scenario and discussing the main assumptions on the system parameters. Then, we present the different steps of the procedure used for the dynamical fitting of the SIRUCQTHE model. Finally, we compare the estimation results with the real data on the COVID-19 secondary waves in Italy. Provided the availability of reliable data to calibrate the model in heterogeneous scenarios, the proposed approach can be easily extended to cope with other scenarios. © 2021 IEEE.}, author_keywords={COVID-19; Dynamical fitting; Estimation; Identification; Pandemic modeling}, keywords={Forecasting, Disease transmission; Epidemiological modeling; Estimation results; Infection rates; Italian regions; Mitigation strategy; Predicting and analyzing; Scenario-based simulations, Shear waves}, references={World Health Organization, , https://www.who.int/emergencies/diseases/novel-coronavirus-2019, Accessed: 2021-01-14; Mojur, M., Fattah, I.R., Alam, M.A., Islam, A.S., Ong, H.C., Rahman, S.A., Naja, G., Mahlia, T., Impact of COVID-19 on the social, economic, environmental and energy domains: Lessons learnt from a global pandemic (2021) Sustainable Production and Consumption, 26, pp. 343-359; Auriemma, V., Iannaccone, C., COVID-19 pandemic: Socioeconomic consequences of social distancing measures in Italy (2020) Fron-tiers in Sociology, 5, p. 78; Paré, P.E., Beck, C.L., Başar, T., Modeling, estimation, and analysis of epidemics over networks: An overview (2020) Annu. Rev. Control, 50, pp. 345-360; Calaore, G.C., Novara, C., Possieri, C., A time-varying sird model for the COVID-19 contagion in Italy (2020) Annual Reviews in Control, 50, pp. 361-372; Ding, Y., Gao, L., An evaluation of COVID-19 in Italy: A data-driven modeling analysis (2020) Infectious Disease Modelling, 5, pp. 495-501; Loli Piccolomini, E., Zama, F., Monitoring Italian COVID-19 spread by a forced seird model (2020) PLoS ONE, 15 (8), p. 0237417; Della Rossa, F., Salzano, D., Di Meglio, A., De Lellis, F., A network model of Italy shows that intermittent regional strategies can alleviate the COVID-19 epidemic (2020) Nat. Commun., 11, p. 5106; Carli, R., Cavone, G., Epicoco, N., Scarabaggio, P., Dotoli, M., Model predictive control to mitigate the COVID-19 outbreak in a multi-region scenario (2020) Annu. Rev. Control, 50, pp. 373-393; Scharbarg, E., Moog, C.H., Mauduit, N., Califano, C., From the hospital scale to nationwide: Observability and identification of models for the COVID-19 epidemic waves (2020) Annu. Rev. Control, , https://doi.org/10.1016/j.arcontrol.2020.09.007, in press; Brugnano, L., Iavernaro, F., Zanzottera, P., A multiregional extension of the sir model, with application to the COVID-19 spread in Italy (2020) Math. Method. Appl. Sci.; Ansumali, S., Kaushal, S., Kumar, A., Prakash, M.K., Vidyasagar, M., Modelling a pandemic with asymptomatic patients, impact of lockdown and herd immunity, with applications to sars-cov-2 (2020) Annu. Rev. Control, , https://doi.org/10.1016/j.arcontrol.2020.10.003, in press; Gatto, M., Bertuzzo, E., Mari, L., Miccoli, S., Spread and dynamics of the COVID-19 epidemic in Italy: Effects of emergency containment measures (2020) Proc. Nat. Acad. Sci., 117 (19), pp. 10484-10491; Scarabaggio, P., Carli, R., Cavone, G., Epicoco, N., Dotoli, M., (2021) Stochastic Non-pharmaceutical Optimal Control Strategies to Mitigate COVID-19, , https://doi.org/10.36227/techrxiv.14413259.v1, TechRxiv preprint; Google COVID-19 Community Mobility Reports, , www.google.com/covid19/mobility, Google LLC, Accessed: 2021-01-14; Bai, Y., Yao, L., Wei, T., Tian, F., Presumed asymptomatic carrier transmission of COVID-19 (2020) JAMA, 323 (14), pp. 1406-1407; The Civil Protection Department, , http://www.protezionecivile.gov.it/, Accessed: 2021-01-14; Hu, H., Nigmatulina, K., Eckhoff, P., The scaling of contact rates with population density for the infectious disease models (2013) Math. Biosci., 244 (2), pp. 125-134; Santamaria, C., Sermi, F., Spyratos, S., Iacus, S.M., Measuring the impact of COVID-19 confinement measures on human mobility using mobile positioning data. A european regional analysis (2020) Saf. Sci., 132, p. 104925; Iacus, S.M., Santamaria, C., Sermi, F., Spyratos, S., Tarchi, D., Vespe, M., Human mobility and COVID-19 initial dynamics (2020) Nonlinear Dynamics, 101 (3), pp. 1901-1919; Guan, W.-J., Ni, Z.-Y., Hu, Y., Liang, W.-H., Clinical characteristics of coronavirus disease 2019 in China (2020) N. Engl. J. Med., 382 (18), pp. 1708-1720; Li, Q., Guan, X., Wu, P., Wang, X., Early transmission dynamics in Wuhan, China, of novel coronavirus. infected pneumonia (2020) N. Engl. J. Med.; Pedersen, M., Meneghini, M., (2020) Quantifying Undetected COVID-19 Cases and Effects of Containment Measures in Italy: Predicting Phase 2 Dynamics, , 03; Ehmann, K.Z., Drosten, C., Wendtner, C., Zange, M., Virological assessment of hospitalized cases of coronavirus disease 2019 (2020) Nature, 581, pp. 465-469; Liu, Y., Yan, L.-M., Wan, L., Xiang, T.-X., Viral dynamics in mild and severe cases of COVID-19 (2020) Lancet Inf. Dis.; Bertozzi, A.L., Franco, E., Mohler, G., Short, M.B., Sledge, D., The challenges of modeling and forecasting the spread of COVID-19 (2020) Proc. Nat. Acad. Sci., 117 (29), pp. 16732-16738; COVID-19: Indicazioni per la Durata e Il Termine della Quarantena, , http://www.salute.gov.it/portale/home.html, Accessed: 2021-01-14; Wei, C., Wang, Z., Liang, Z., Liu, Q., The focus and timing of COVID-19 pandemic control measures under healthcare resource constraints (2020) MedRxiv; Lemos-Paiao, A.P., Silva, C.J., Torres, D.F., A new compartmental epidemiological model for COVID-19 with a case study of Portugal (2020) Ecol. Complex., p. 100885; Giordano, G., Blanchini, F., Bruno, R., Colaneri, P., Modelling the COVID-19 epidemic and implementation of population-wide interventions in Italy (2020) Nat. Med., 26, pp. 855-860; Zhou, F., Yu, T., Du, R., Fan, G., Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: A retrospective cohort study (2020) Lancet; Aspelund, K., Droste, M., Stock, J.H., Walker, C.D., Identification and estimation of undetected COVID-19 cases using testing data from Iceland (2020) NBER Work. Pap. No. w27528; Decision & Control Laboratory, , http://dclab.poliba.it/covid-19, Accessed: 2021-04-27}, document_type={Conference Paper}, source={Scopus}, }`

- Hosseini, S. M., Carli, R. & Dotoli, M. (2021) Robust Optimal Energy Management of a Residential Microgrid under Uncertainties on Demand and Renewable Power Generation. IN IEEE Transactions on Automation Science and Engineering, 18.618-637.

[Bibtex]`@ARTICLE{Hosseini2021618, author={Hosseini, S.M. and Carli, R. and Dotoli, M.}, title={Robust Optimal Energy Management of a Residential Microgrid under Uncertainties on Demand and Renewable Power Generation}, journal={IEEE Transactions on Automation Science and Engineering}, year={2021}, volume={18}, number={2}, pages={618-637}, doi={10.1109/TASE.2020.2986269}, art_number={9093973}, note={cited By 27}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100266630&doi=10.1109%2fTASE.2020.2986269&partnerID=40&md5=ab5c0a8a4eb48977e42d7186b3d4fc3b}, abstract={Smart microgrids are experiencing an increasing growth due to their economic, social, and environmental benefits. However, the inherent intermittency of renewable energy sources (RESs) and users' behavior lead to significant uncertainty, which implies important challenges on the system design. Facing this issue, this article proposes a novel robust framework for the day-ahead energy scheduling of a residential microgrid comprising interconnected smart users, each owning individual RESs, noncontrollable loads (NCLs), energy- and comfort-based CLs, and individual plug-in electric vehicles (PEVs). Moreover, users share a number of RESs and an energy storage system (ESS). We assume that the microgrid can buy/sell energy from/to the grid subject to quadratic/linear dynamic pricing functions. The objective of scheduling is minimizing the expected energy cost while satisfying device/comfort/contractual constraints, including feasibility constraints on energy transfer between users and the grid under RES generation and users' demand uncertainties. To this aim, first, we formulate a min-max robust problem to obtain the optimal CLs scheduling and charging/discharging strategies of the ESS and PEVs. Then, based on the duality theory for multi-objective optimization, we transform the min-max problem into a mixed-integer quadratic programming problem to solve the equivalent robust counterpart of the scheduling problem effectively. We deal with the conservativeness of the proposed approach for different scenarios and quantify the effects of the budget of uncertainty on the cost saving, the peak-to-average ratio, and the constraints' violation rate. We validate the effectiveness of the method on a simulated case study and we compare the results with a related robust approach. Note to Practitioners - This article is motivated by the emerging need for intelligent demand-side management (DSM) approaches in smart microgrids in the presence of both power generation and demand uncertainties. The proposed robust energy scheduling strategy allows the decision maker (i.e., the energy manager of the microgrid) to make a satisfactory tradeoff between the users' payment and constraints' violation rate considering the energy cost saving, the system technical limitations and the users' comfort by adjusting the values of the budget of uncertainty. The proposed framework is generic and flexible as it can be applied to different structures of microgrids considering various types of uncertainties in energy generation or demand. © 2004-2012 IEEE.}, author_keywords={Demand-side management (DSM); microgrid; mixed-integer quadratic programming (MIQP); optimal energy scheduling; optimization; robust control}, keywords={Budget control; Costs; Decision making; Electric utilities; Electromagnetic wave emission; Energy storage; Energy transfer; Housing; Integer programming; Microgrids; Multiobjective optimization; Plug-in electric vehicles; Quadratic programming; Renewable energy resources; Scheduling, Energy storage systems; Environmental benefits; Mixed integer quadratic programming; Peak to average ratios; Renewable energy source; Renewable power generation; Scheduling strategies; Technical limitations, Demand side management}, references={Tan, Y., Cao, Y., Li, Y., Lee, K.Y., Jiang, L., Li, S., Optimal day-ahead operation considering power quality for active distribution networks (2017) IEEE Trans. Autom. Sci. Eng., 14 (2), pp. 425-436. , Apr; Brusco, G., Burgio, A., Menniti, D., Pinnarelli, A., Sorrentino, N., Energy management system for an energy district with demand response availability (2014) IEEE Trans. Smart Grid, 5 (5), pp. 2385-2393. , Sep; Fujimoto, Y., Distributed energy management for comprehensive utilization of residential photovoltaic outputs (2018) IEEE Trans. Smart Grid, 9 (2), pp. 1216-1227. , Mar; Yousefi, M., Hajizadeh, A., Soltani, M., Energy management strategies for smart home regarding uncertainties: State of the art, trends, and challenges (2018) Proc. IEEE Int. Conf. Ind. Technol. (ICIT), pp. 1219-1225. , Feb; Rahmani-Andebili, M., Shen, H., Energy scheduling for a smart home applying stochastic model predictive control (2016) Proc. 25th Int. Conf. Comput. Commun. Netw. (ICCCN), pp. 1-6. , Waikoloa, HI, USA, Aug; Yang, Y., Jia, Q.-S., Guan, X., Zhang, X., Qiu, Z., Deconinck, G., Decentralized EV-based charging optimization with building integrated wind energy (2019) IEEE Trans. Autom. Sci. Eng., 16 (3), pp. 1002-1017. , Jul; Teng, F., Strbac, G., Full stochastic scheduling for low-carbon electricity systems (2017) IEEE Trans. Autom. Sci. Eng., 14 (2), pp. 461-470. , Apr; Aghajani, G.R., Shayanfar, H.A., Shayeghi, H., Demand side management in a smart micro-grid in the presence of renewable generation and demand response (2017) Energy, 126, pp. 622-637. , May; Yan, B., Luh, P.B., Warner, G., Zhang, P., Operation and design optimization of microgrids with renewables (2017) IEEE Trans. Autom. Sci. Eng., 14 (2), pp. 573-585. , Apr; Verrilli, F., Model predictive control-based optimal operations of district heating system with thermal energy storage and flexible loads (2017) IEEE Trans. Autom. Sci. Eng., 14 (2), pp. 547-557. , Apr; Ouammi, A., Achour, Y., Zejli, D., Dagdougui, H., Supervisory model predictive control for optimal energy management of networked smart greenhouses integrated microgrid (2020) IEEE Trans. Autom. Sci. Eng., 17 (1), pp. 117-128. , Jan; Carli, R., Dotoli, M., Energy scheduling of a smart home under nonlinear pricing (2014) Proc. 53rd IEEE Conf. Decis. Control, pp. 5648-5653. , Los Angeles, CA, USA, Dec; Carli, R., Dotoli, M., Decentralized control for residential energy management of a smart users microgrid with renewable energy exchange (2019) IEEE/CAA J. Automatica Sinica, 6 (3), pp. 641-656. , May; Wang, C., Zhou, Y., Wu, J., Wang, J., Zhang, Y., Wang, D., Robustindex method for household load scheduling considering uncertainties of customer behavior (2015) IEEE Trans. Smart Grid, 6 (4), pp. 1806-1818. , Jul; Kim, B.-G., Zhang, Y., Van-Der-Schaar, M., Lee, J.-W., Dynamic pricing and energy consumption scheduling with reinforcement learning (2016) IEEE Trans. Smart Grid, 7 (5), pp. 2187-2198. , Sep; Mohsenian-Rad, A.-H., Wong, V.W.S., Jatskevich, J., Schober, R., Optimal and autonomous incentive-based energy consumption scheduling algorithm for smart grid (2010) Proc. Innov. Smart Grid Technol. (ISGT), pp. 1-6. , Gothenburg, Sweden, Jan; Yue, J., Hu, Z., Anvari-Moghaddam, A., Guerrero, J.M., A multimarket- driven approach to energy scheduling of smart microgrids in distribution networks (2019) Sustainability, 11 (2), p. 301. , Jan; Rajasekhar, B., Pindoriya, N., Tushar, W., Yuen, C., Collaborative energy management for a residential community: A non-cooperative and evolutionary approach (2019) IEEE Trans. Emerg. Topics Comput. Intell., 3 (3), pp. 177-192. , Jun; Samadi, P., Schober, R., Wong, V.W.S., Optimal energy consumption scheduling using mechanism design for the future smart grid (2011) Proc. IEEE Int. Conf. Smart Grid Commun. (SmartGridComm), pp. 369-374. , Brussels, Belgium, Oct; Tushar, M.H.K., Assi, C., Maier, M., Uddin, M.F., Smart microgrids: Optimal joint scheduling for electric vehicles and home appliances (2014) IEEE Trans. Smart Grid, 5 (1), pp. 239-250. , Jan; Yue, J., Hu, Z., Li, C., Vasquez, J.C., Guerrero, J.M., Economic power schedule and transactive energy through an intelligent centralized energy management system for a DC residential distribution system (2017) Energies, 10 (7), p. 916. , Jul; Kim, T.T., Poor, H.V., Scheduling power consumption with price uncertainty (2011) IEEE Trans. Smart Grid, 2 (3), pp. 519-527. , Sep; Wu, X., Hu, X., Yin, X., Moura, S.J., Stochastic optimal energy management of smart home with PEV energy storage (2018) IEEE Trans. Smart Grid, 9 (3), pp. 2065-2075. , May; Munkhammar, J., Widén, J., Rydén, J., On a probability distribution model combining household power consumption, electric vehicle homecharging and photovoltaic power production (2015) Appl. Energy, 142, pp. 135-143. , Mar; Kou, P., Liang, D., Gao, L., Stochastic energy scheduling in microgrids considering the uncertainties in both supply and demand (2018) IEEE Syst. J., 12 (3), pp. 2589-2600. , Sep; Chen, Z., Wu, L., Fu, Y., Real-time price-based demand response management for residential appliances via stochastic optimization and robust optimization (2012) IEEE Trans. Smart Grid, 3 (4), pp. 1822-1831. , Dec; Guo, L., Wu, H.-C., Zhang, H., Xia, T., Mehraeen, S., Robust optimization for home-load scheduling under price uncertainty in smart grids (2015) Proc. Int. Conf. Comput., Netw. Commun. (ICNC), pp. 487-493. , Garden Grove, CA, USA, Feb; Zhang, C., Xu, Y., Dong, Z.Y., Ma, J., Robust operation of microgrids via two-stage coordinated energy storage and direct load control (2017) IEEE Trans. Power Syst., 32 (4), pp. 2858-2868. , Jul; Yi, W., Zhang, Y., Zhao, Z., Huang, Y., Multiobjective robust scheduling for smart distribution grids: Considering renewable energy and demand response uncertainty (2018) IEEE Access, 6, pp. 45715-45724; Wang, C., Zhou, Y., Jiao, B., Wang, Y., Liu, W., Wang, D., Robust optimization for load scheduling of a smart home with photovoltaic system (2015) Energy Convers. Manage., 102, pp. 247-257. , Sep; Paul, S., Padhy, N.P., Resilient scheduling portfolio of residential devices and plug-in electric vehicle by minimizing conditional value at risk (2019) IEEE Trans. Ind. Informat., 15 (3), pp. 1566-1578. , Mar; Hussain, A., Bui, V.-H., Kim, H.-M., Robust optimal operation of AC/DC hybrid microgrids under market price uncertainties (2018) IEEE Access, 6, pp. 2654-2667; Paridari, K., Parisio, A., Sandberg, H., Johansson, K.H., Robust scheduling of smart appliances in active apartments with user behavior uncertainty (2016) IEEE Trans. Autom. Sci. Eng., 13 (1), pp. 247-259. , Jan; Doulabi, H.H., Jaillet, P., Pesant, G., Rousseau, L.M., Exploiting the structure of two-stage robust optimization models with exponential scenarios INFORMS J. Comput., , to be published; Zeng, B., Zhao, L., Solving two-stage robust optimization problems using a column-and-constraint generation method (2013) Oper. Res. Lett., 41 (5), pp. 457-461. , Sep; Ben-Tal, A., Goryashko, A., Guslitzer, E., Nemirovski, A., Adjustable robust solutions of uncertain linear programs (2004) Math. Program., 99 (2), pp. 351-376. , Mar; Aharon, B.-T., Boaz, G., Shimrit, S., Robust multi-echelon multiperiod inventory control (2009) Eur. J. Oper. Res., 199 (3), pp. 922-935. , Dec; Ouorou, A., Tractable approximations to a robust capacity assignment model in telecommunications under demand uncertainty (2013) Comput. Oper. Res., 40 (1), pp. 318-327. , Jan; Bertsimas, D., Brown, D., Caramanis, C., Theory and applications of robust optimization (2011) SIAM Rev., 53 (3), pp. 464-501; De Ruiter, F.J.C.T., Ben-Tal, A., Brekelmans, R.C.M., Hertog, D.D., Robust optimization of uncertain multistage inventory systems with inexact data in decision rules (2017) Comput. Manag. Sci., 14 (1), pp. 45-66; Bertsimas, D., Sim, M., The price of robustness (2004) Oper. Res., 52 (1), pp. 35-53. , Feb; Wu, X., Wang, Z., Du, J., Wu, G., Optimal operation of residential microgrids in the Harbin area (2018) IEEE Access, 6, pp. 30726-30736; Ahmadi, M., Rosenberger, J.M., Lee, W., Kulvanitchaiyanunt, A., Optimizing load control in a collaborative residential microgrid environment (2015) IEEE Trans. Smart Grid, 6 (3), pp. 1196-1207. , May; Yang, X., Zhang, Y., Wu, H., He, H., An event-driven ADR approach for residential energy resources in microgrids with uncertainties (2019) IEEE Trans. Ind. Electron., 66 (7), pp. 5275-5288. , Jul; Anvari-Moghaddam, A., Guerrero, J.M., Vasquez, J.C., Monsef, H., Rahimi-Kian, A., Efficient energy management for a grid-tied residential microgrid (2017) IET Gener., Transmiss. Distrib., 11 (11), pp. 2752-2761. , Aug; Orwig, K.D., Recent trends in variable generation forecasting and its value to the power system (2015) IEEE Trans. Sustain. Energy, 6 (3), pp. 924-933. , Jul; Kong, W., Dong, Z.Y., Hill, D.J., Luo, F., Xu, Y., Short-term residential load forecasting based on resident behaviour learning (2018) IEEE Trans. Power Syst., 33 (1), pp. 1087-1088. , Jan; Esther, B.P., Kumar, K.S., A survey on residential demand side management architecture, approaches, optimization models and methods (2016) Renew. Sustain. Energy Rev., 59, pp. 342-351. , Jun; Ramanathan, B., Vittal, V., A framework for evaluation of advanced direct load control with minimum disruption (2008) IEEE Trans. Power Syst., 23 (4), pp. 1681-1688. , Nov; Monteiro, V., Gonçalves, H., Ferreira, J.C., Afonso, J.L., Carmo, J.P., Ribeiro, J.E., Batteries charging systems for electric and plug-in hybrid electric vehicles (2012) New Advances in Vehicular Technology and Automotive Engineering, pp. 149-168. , Rijeka, Croatia: InTech; Parisio, A., Rikos, E., Glielmo, L., A model predictive control approach to microgrid operation optimization (2014) IEEE Trans. Control Syst. Technol., 22 (5), pp. 1813-1827. , Sep; Liu, Z., Zhang, C., Dong, M., Gu, B., Ji, Y., Tanaka, Y., Markovdecision- process-assisted consumer scheduling in a networked smart grid (2017) IEEE Access, 5, pp. 2448-2458; Bemporad, A., Morari, M., Control of systems integrating logic, dynamics, and constraints (1999) Automatica, 35 (3), pp. 407-427. , Mar; Wanka, G., Bot, R.-I., Multiobjective duality for convex-linear problems II (2001) Math. Methods Operations Res., 53 (3), pp. 419-433. , Jul; Rezzonico, S., Nowak, S., (1997) Buy-back Rates for Grid-connected Photovoltaic Power Systems, , Int. Energy Agency, Saint Ursen, Switzerland, Tech. Rep. IEA PVPS TI 1997 2, Nov; (2007) Electricity Prices for Household Consumers-Bi- Annual Data, , http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=nrg_pc_204&lang=en, Accessed: Apr. 30, 2019. [Online]; Alberini, A., Prettico, G., Shen, C., Torriti, J., Hot weather and hourly electricity demand in Italy (2019) Energy, 177, pp. 44-56. , Jun; Tutkun, N., Can, O., San, E.S., Daily cost minimization for an offgrid renewable microhybrid system installed to a residential home (2015) Proc. Int. Conf. Renew. Energy Res. Appl. (ICRERA), pp. 750-754. , Palermo, Italy, Nov; Soyster, A.L., Technical note-convex programming with set-inclusive constraints and applications to inexact linear programming (1973) Oper. Res., 21 (5), pp. 1154-1157. , Oct; Hubert, T., Grijalva, S., Modeling for residential electricity optimization in dynamic pricing environments (2012) IEEE Trans. Smart Grid, 3 (4), pp. 2224-2231. , Dec}, document_type={Article}, source={Scopus}, }`

- Helmi, A. M., Carli, R., Dotoli, M. & Ramadan, H. S. (2021) Efficient and Sustainable Reconfiguration of Distribution Networks via Metaheuristic Optimization. IN IEEE Transactions on Automation Science and Engineering, ..

[Bibtex]`@ARTICLE{Helmi2021, author={Helmi, A.M. and Carli, R. and Dotoli, M. and Ramadan, H.S.}, title={Efficient and Sustainable Reconfiguration of Distribution Networks via Metaheuristic Optimization}, journal={IEEE Transactions on Automation Science and Engineering}, year={2021}, doi={10.1109/TASE.2021.3072862}, note={cited By 3}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105879516&doi=10.1109%2fTASE.2021.3072862&partnerID=40&md5=1b9b5290b92a3a5ad8886f61ec903dc8}, abstract={Improving the efficiency and sustainability of distribution networks (DNs) is nowadays a challenging objective both for large networks and microgrids connected to the main grid. In this context, a crucial role is played by the so-called network reconfiguration problem, which aims at determining the optimal DN topology. This process is enabled by properly changing the close/open status of all available branch switches to form an admissible graph connecting network buses. The reconfiguration problem is typically modeled as an NP-hard combinatorial problem with a complex search space due to current and voltage constraints. Even though several metaheuristic algorithms have been used to obtain--without guarantees--the global optimal solution, searching for near-optimal solutions in reasonable time is still a research challenge for the DN reconfiguration problem. Facing this issue, this article proposes a novel effective optimization framework for the reconfiguration problem of modern DNs. The objective of reconfiguration is minimizing the overall power losses while ensuring an enhanced DN voltage profile. A multiple-step resolution procedure is then presented, where the recent Harris hawks optimization (HHO) algorithm constitutes the core part. This optimizer is here intelligently accompanied by appropriate preprocessing (i.e., search space preparation and initial feasible population generation) and postprocessing (i.e., solution refinement) phases aimed at improving the search for near-optimal configurations. The effectiveness of the method is validated through numerical experiments on the IEEE 33-bus, the IEEE 85-bus systems, and an artificial 295-bus system under distributed generation and load variation. Finally, the performance of the proposed HHO-based approach is compared with two related metaheuristic techniques, namely the particle swarm optimization algorithm and the Cuckoo search algorithm. The results show that HHO outperforms the other two optimizers in terms of minimized power losses, enhanced voltage profile, and running time. IEEE}, author_keywords={Distribution network (DN) reconfiguration; Harris hawks optimization (HHO) algorithm; metaheuristic optimization; Microgrids; microgrids; Optimization; power losses reduction; Search problems; Sociology; Statistics; Sustainable development; Topology; voltage profile improvement.}, keywords={Microgrids; NP-hard; Numerical methods; Optimal systems; Particle swarm optimization (PSO), Cuckoo search algorithms; Global optimal solutions; Meta heuristic algorithm; Meta-heuristic optimizations; Meta-heuristic techniques; Network re-configuration; Particle swarm optimization algorithm; Reconfiguration of distribution networks, Internet protocols}, document_type={Article}, source={Scopus}, }`

- Scarabaggio, P., Grammatico, S., Carli, R. & Dotoli, M. (2021) Distributed Demand Side Management With Stochastic Wind Power Forecasting. IN IEEE Transactions on Control Systems Technology, ..

[Bibtex]`@ARTICLE{Scarabaggio2021, author={Scarabaggio, P. and Grammatico, S. and Carli, R. and Dotoli, M.}, title={Distributed Demand Side Management With Stochastic Wind Power Forecasting}, journal={IEEE Transactions on Control Systems Technology}, year={2021}, doi={10.1109/TCST.2021.3056751}, note={cited By 18}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100918524&doi=10.1109%2fTCST.2021.3056751&partnerID=40&md5=4f491e205e325ad467291e71ce8dbff8}, abstract={In this article, we propose a distributed demand-side management (DSM) approach for smart grids taking into account uncertainty in wind power forecasting. The smart grid model comprehends traditional users as well as active users (prosumers). Through a rolling-horizon approach, prosumers participate in a DSM program, aiming at minimizing their cost in the presence of uncertain wind power generation by a game theory approach. We assume that each user selfishly formulates its grid optimization problem as a noncooperative game. The core challenge in this article is defining an approach to cope with the uncertainty in wind power availability. We tackle this issue from two different sides: by employing the expected value to define a deterministic counterpart for the problem and by adopting a stochastic approximated framework. In the latter case, we employ the sample average approximation (SAA) technique, whose results are based on a probability density function (PDF) for the wind speed forecasts. We improve the PDF by using historical wind speed data, and by employing a control index that takes into account the weather condition stability. Numerical simulations on a real data set show that the proposed stochastic strategy generates lower individual costs compared to the standard expected value approach. IEEE}, author_keywords={Demand-side management (DSM); model predictive control; Optimization; sample average approximation (SAA); smart grid; Smart grids; stochastic optimization.; Stochastic processes; Uncertainty; Wind forecasting; Wind power generation; Wind speed}, keywords={Electric power generation; Electric power transmission networks; Electric utilities; Game theory; Probability density function; Smart power grids; Stochastic systems; Weather forecasting; Wind; Wind power, Grid optimization; Noncooperative game; Probability density function (pdf); Sample average approximation; Stochastic winds; Wind power availability; Wind power forecasting; Wind speed forecast, Demand side management}, document_type={Article}, source={Scopus}, }`

### 2020

- Dotoli, M. & Epicoco, N. (2020) Integrated Network Design of Agile Resource-Efficient Supply Chains under Uncertainty. IN IEEE Transactions on Systems, Man, and Cybernetics: Systems, 50.4530-4544.

[Bibtex]`@ARTICLE{Dotoli20204530, author={Dotoli, M. and Epicoco, N.}, title={Integrated Network Design of Agile Resource-Efficient Supply Chains under Uncertainty}, journal={IEEE Transactions on Systems, Man, and Cybernetics: Systems}, year={2020}, volume={50}, number={11}, pages={4530-4544}, doi={10.1109/TSMC.2018.2854620}, art_number={8421073}, note={cited By 5}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85050603527&doi=10.1109%2fTSMC.2018.2854620&partnerID=40&md5=7ca65828d2287dd518f0fe708d036197}, abstract={We present a novel method for supply chain network (SCN) design under uncertainty that jointly solves the candidate selection, the order allocation, and the transportation mode selection problems. In the proposed method, four steps are executed in cascade. First, a cross-efficiency fuzzy data envelopment analysis technique ranks the candidates of each SCN stage in a multiobjective perspective and under uncertain data. Second, a fuzzy linear integer programming model determines the supplies required from each actor by those belonging to the subsequent SCN stage. This step determines the best compromise between candidates' efficiencies, estimated costs, and delivery time, considering stock levels and uncertain capacity of actors, while satisfying customers' uncertain demand. The third step evaluates the efficiency of the transportation alternatives under uncertain data to optimally plan the transport chain. Finally, the fourth step measures the performance of the designed SCN. The method provides as a result an integrated, agile, and resource-efficient design of the SCN under uncertainty. Its application to a case study shows it is effective in selecting the SCN partners, assigning the corresponding order quantities, and delivering them to customers. Validation is obtained by comparison with well-known approaches and statistical analysis. © 2018 IEEE.}, author_keywords={Agility; data envelopment analysis (DEA); efficiency; fuzzy set theory; supply chain network (SCN) design}, keywords={Data envelopment analysis; Decision making; Decision theory; Efficiency; Fuzzy set theory; Integer programming; Linear programming; Random processes; Stochastic systems; Supply chains; Transportation; Uncertainty analysis, Agility; Candidate selection; Design under uncertainty; Fuzzy data envelopment analysis; Linear integer programming; Resource management; Supply chain network; Uncertainty, Information management}, references={Choi, T.-M., Supply chain systems coordination with multiple risk sensitive retail buyers (2016) IEEE Trans. Syst., Man, Cybern., Syst., 46 (5), pp. 636-645. , May; Russo, M., Cesarani, M., Strategic alliance success factors: A literature review on alliance lifecycle (2017) Int. J. Bus. Admin., 8 (3), pp. 1-9; Wu, D.D., Luo, C., Olson, D.L., Efficiency evaluation for supply chains using maximin decision support (2014) IEEE Trans. Syst., Man, Cybern., Syst., 44 (8), pp. 1088-1097. , Aug; Yan, J., Li, X., Sun, S.X., Shi, Y., Wang, H., A BDI modeling approach for decision support in supply chain quality inspection IEEE Trans. Syst., Man, Cybern., Syst., to Be Published; Heckmann, I., Comes, T., Nickel, S., A critical review on supply chain risk-Definition, measure and modeling (2015) Omega, 52, pp. 119-132. , Apr; Dubey, R., Antecedents of resilient supply chains: An empirical study IEEE Trans. Eng. Manag., to Be Published; Wang, J.-J., Dong, J., Yue, X., Zhong, Q., Information sharing in a supply chain with a coopetitive contract manufacturer IEEE Trans. Syst., Man, Cybern., Syst., to Be Published; Matopoulos, A., Barros, A.C., Vorst, J.A.J.G., Resourceefficient supply chains: A research framework, literature review and research agenda (2015) Supply Chain Manag. Int. J., 20 (2), pp. 218-236; Dotoli, M., Epicoco, N., Falagario, M., A fuzzy technique for supply chain network design with quantity discounts (2017) Int. J. Prod. Res., 55 (7), pp. 1862-1884; Dias, L.S., Ierapetritou, M.G., From process control to supply chain management: An overview of integrated decision making strategies (2017) Comput. Chem. Eng., 106, pp. 826-835. , Nov; Hofmann, E., Rüsch, M., Industry 4.0 and the current status as well as future prospects on logistics (2017) Comput. Ind., 89, pp. 23-34. , Aug; Wang, M., Liu, K., Choi, T.-M., Yue, X., Effects of carbon emission taxes on transportation mode selections and social welfare (2015) IEEE Trans. Syst., Man, Cybern., Syst., 45 (11), pp. 1413-1423. , Nov; Babazadeh, R., Razmi, J., Rabbani, M., Pishvaee, M.S., An integrated data envelopment analysis-mathematical programming approach to strategic biodiesel supply chain network design problem (2015) J. Clean. Prod., 147, pp. 694-707. , Mar; Ray, A., De, A., Dan, P.K., Facility location selection using complete and partial ranking MCDM methods (2015) Int. J. Ind. Syst. Eng., 19 (2), pp. 262-276; Eskandarpour, M., Dejax, P., Péton, O., A large neighborhood search heuristic for supply chain network design (2017) Comput. Oper. Res., 80, pp. 23-37. , Apr; Zhen, L., Wang, W., Zhuge, D., Optimizing locations and scales of distribution centers under uncertainty (2017) IEEE Trans. Syst., Man, Cybern., Syst., 47 (11), pp. 2908-2919. , Nov; De, A., Mamanduru, V.K.R., Gunasekaran, A., Subramanian, N., Tiwari, M.K., Composite particle algorithm for sustainable integrated dynamic ship routing and scheduling optimization (2016) Comput. Ind. Eng., 96, pp. 201-215. , Jun; Li, M., Wang, Z., Chan, F.T.S., An inventory routing policy under replenishment lead time (2017) IEEE Trans. Syst., Man, Cybern., Syst., 47 (12), pp. 3150-3164. , Dec; Babazadeh, R., Razmi, J., Pishvaee, M.S., Rabbani, M., A sustainable second-generation biodiesel supply chain network design problem under risk (2017) Omega, 66, pp. 258-277. , Jan; Rahmani, D., Mahoodian, V., Strategic and operational supply chain network design to reduce carbon emission considering reliability and robustness (2017) J. Clean. Prod., 149, pp. 607-620. , Apr; Wang, G., Gunasekaran, A., Modeling and analysis of sustainable supply chain dynamics (2017) Ann. Oper. Res., 250 (2), pp. 521-536; Zhang, J., Wei, Q., Liu, G., Tang, W., A supplier switching model with the competitive reactions and economies of scale effects (2017) IEEE Trans. Syst., Man, Cybern., Syst., 47 (11), pp. 2831-2843. , Nov; Kumar, D., Rahman, Z., Chan, F.T.S., A fuzzy AHP and fuzzy multiobjective linear programming model for order allocation in a sustainable supply chain: A case study (2017) Int. J. Comput. Integr. Manuf., 30 (6), pp. 535-551; Govindan, K., Fattahi, M., Keyvanshokooh, E., Supply chain network design under uncertainty: A comprehensive review and future research directions (2017) Eur. J. Oper. Res., 263 (1), pp. 108-141; Dotoli, M., Epicoco, N., Falagario, M., Sciancalepore, F., A crossefficiency fuzzy data envelopment analysis technique for performance evaluation of decision making units under uncertainty (2015) Comput. Ind. Eng., 79, pp. 103-114. , Jan; Dotoli, M., Epicoco, N., Falagario, M., Integrated supplier selection and order allocation under uncertainty in agile supply chains (2015) Proc. IEEE Int. Conf. Emerg. Technol. Factory Autom. (ETFA), pp. 3477-3482. , Oct; Chow, P.-S., Choi, T.-M., Cheng, T.C.E., Impacts of minimum order quantity on a quick response supply chain (2012) IEEE Trans. Syst., Man, Cybern. A, Syst., Humans, 42 (4), pp. 868-879. , Jul; Costantino, N., A hierarchical optimization technique for the strategic design of distribution networks (2013) Comput. Ind. Eng., 66 (4), pp. 849-864; Dotoli, M., Epicoco, N., Falagario, M., Sciancalepore, F., A stochastic cross-efficiency data envelopment analysis approach for supplier selection under uncertainty (2016) Int. Trans. Oper. Res., 23 (4), pp. 725-748; Amindoust, A., Ahmed, S., Saghafinia, A., Supplier selection and order allocation scenarios in supply chain: A review (2013) Eng. Manag. Rev., 2 (3), pp. 75-80; Matinrad, N., Roghanian, E., Rasi, Z., Supply chain network optimization: A review of classification, models, solution techniques and future research (2013) Uncertain Supply Chain Manag., 1 (1), pp. 1-24; Kao, T.-W.D., Simpson, N.C., Shao, B.B.M., Lin, W.T., Relating supply network structure to productive efficiency: A multi-stage empirical investigation (2017) Eur. J. Oper. Res., 259 (2), pp. 469-485; Scott, J., Ho, W., Dey, P.K., Talluri, S., A decision support system for supplier selection and order allocation in stochastic, multistakeholder and multi-criteria environments (2015) Int. J. Prod. Econ., 166, pp. 226-237. , Aug; Velasquez, M., Hester, P.T., An analysis of multi-criteria decision making methods (2013) Int. J. Oper. Res., 10 (2), pp. 56-66; Charnes, A., Cooper, W.W., Rhodes, E., Measuring the efficiency of decision making units (1978) Eur. J. Oper. Res., 2 (6), pp. 429-444; Wei, G., Wang, J., A comparative study of robust efficiency analysis and data envelopment analysis with imprecise data (2017) Expert Syst. Appl., 81, pp. 28-38. , Sep; Shahin, A., Khalili, A., Pourhamidi, M., Proposing a new approach for evaluating supply chain agility by data envelopment analysis with a case study in Pashmineh Kavir factory (2017) Int. J. Services Oper. Manag., 26 (3), pp. 277-293; Sexton, T.R., Silkman, R.H., Hogan, J.A., Data envelopment analysis: Critique and extensions (1986) New Directions Program Eval., 1986 (32), pp. 73-105; Sengupta, J.K., A fuzzy systems approach in data envelopment analysis (1992) Comput. Math. Appl., 24 (8-9), pp. 259-266; Zimmermann, H.J., Fuzzy set theory (2010) Wiley Interdiscipl. Rev. Comput. Stat., 2 (3), pp. 317-332; Ghorabaee, M.K., Amiri, M., Zavadskas, E.K., Antucheviciene, J., Supplier evaluation and selection in fuzzy environments: A review of MADM approaches (2017) Econ. Res., 30 (1), pp. 1073-1118; Karsak, E.E., Dursun, M., Taxonomy and review of nondeterministic analytical methods for supplier selection (2016) Int. J. Comput. Integr. Manuf., 29 (3), pp. 263-286; Kao, C., Network data envelopment analysis with fuzzy data (2014) Performance Measurement with Fuzzy Data Envelopment Analysis, Studies in Fuzziness and Soft Computing, pp. 191-206. , A. Emrouznejad and M. Tavana, Eds. Heidelberg, Germany: Springer; Lozano, S., Moreno, P., Network fuzzy data envelopment analysis (2014) Performance Measurement with Fuzzy Data Envelopment Analysis, Studies in Fuzziness and Soft Computing, pp. 207-230. , A. Emrouznejad and M. Tavana, Eds. Heidelberg, Germany: Springer; Dotoli, M., Falagario, M., A hierarchical model for optimal supplier selection in multiple sourcing contexts (2012) Int. J. Prod. Res., 50 (11), pp. 2953-2967; Azadnia, A.H., Saman, M.Z.M., Wong, K.Y., Sustainable supplier selection and order lot-sizing: An integrated multi-objective decision-making process (2015) Int. J. Prod. Res., 53 (2), pp. 383-408; Sadic, S., Sousa, J.P., Crispim, J.A., A two-phase MILP approach to integrate order, customer and manufacturer characteristics into dynamic manufacturing network formation and operational planning (2018) Expert Syst. Appl., 96, pp. 462-478. , Apr; Firouz, M., Keskin, B.B., Melouk, S.H., An integrated supplier selection and inventory problem with multi-sourcing and lateral transshipments (2017) Omega, 70, pp. 77-93. , Jul; Meixell, M.J., Norbis, M., A review of the transportation mode choice and carrier selection literature (2008) Int. J. Logist. Manag., 19 (2), pp. 183-211; Dotoli, M., Epicoco, N., Falagario, M., A technique for efficient multimodal transport planning with conflicting objectives under uncertainty (2016) Proc. Eur. Control Conf., Aalborg, Denmark, pp. 2441-2446; Costantino, N., Dotoli, M., Epicoco, N., Falagario, M., Sciancalepore, F., A novel fuzzy data envelopment analysis methodology for performance evaluation in a two-stage supply chain (2012) Proc. IEEE Int. Conf. Autom. Sci. Eng., Seoul, South Korea, pp. 974-979; Costantino, N., Dotoli, M., Falagario, M., Fanti, M.P., Using fuzzy decision making for supplier selection in public procurement (2011) J. Public Procurement, 11 (3), pp. 403-427; Wang, Y.-M.M., Luo, Y., Liang, L., Fuzzy data envelopment analysis based upon fuzzy arithmetic with an application to performance assessment of manufacturing enterprises (2009) Expert Syst. Appl., 36 (3), pp. 5205-5211; Luukka, P., Fuzzy similarity in multicriteria decision-making problem applied to supplier evaluation and selection in supply chain management (2011) Adv. Artif. Intell., 2011, pp. 1-9. , Nov; Law, A.M., (2015) Simulation Modeling and Analysis, 5th Ed, , New York, NY, USA: McGraw-Hill}, document_type={Article}, source={Scopus}, }`

- Hosseini, S. M., Carli, R., Parisio, A. & Dotoli, M. (2020) Robust Decentralized Charge Control of Electric Vehicles under Uncertainty on Inelastic Demand and Energy Pricing. IN IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2020-October.1834-1839.

[Bibtex]`@ARTICLE{Hosseini20201834, author={Hosseini, S.M. and Carli, R. and Parisio, A. and Dotoli, M.}, title={Robust Decentralized Charge Control of Electric Vehicles under Uncertainty on Inelastic Demand and Energy Pricing}, journal={IEEE Transactions on Systems, Man, and Cybernetics: Systems}, year={2020}, volume={2020-October}, pages={1834-1839}, doi={10.1109/SMC42975.2020.9283440}, art_number={9283440}, note={cited By 0}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098877625&doi=10.1109%2fSMC42975.2020.9283440&partnerID=40&md5=51febb550a1cfd7909fd0ff26527f5b0}, abstract={This paper proposes a novel robust decentralized charging strategy for large-scale EV fleets. The system incorporates multiple EVs as well as inelastic loads connected to the power grid under power flow limits. We aim at minimizing both the overall charging energy payment and the aggregated battery degradation cost of EVs while preserving the robustness of the solution against uncertainties in the price of the electricity purchased from the power grid and the demand of inelastic loads. The proposed approach relies on the so-called uncertainty set-based robust optimization. The resulting charge scheduling problem is formulated as a tractable quadratic programming problem where all the EVs' decisions are coupled via the grid resource-sharing constraints and the robust counterpart supporting constraints. We adopt an extended Jacobi-Proximal Alternating Direction Method of Multipliers algorithm to solve effectively the formulated scheduling problem in a decentralized fashion, thus allowing the method applicability to large scale fleets. Simulations of a realistic case study show that the proposed approach not only reduces the costs of the EV fleet, but also maintains the robustness of the solution against perturbations in different uncertain parameters, which is beneficial for both EVs' users and the power grid. © 2020 IEEE.}, author_keywords={ADMM; Charge scheduling; Decentralized control; Electric vehicles; Large-scale optimization; Robust optimization; Set-based uncertainty}, keywords={Charging (batteries); Costs; Electric load flow; Quadratic programming; Scheduling; Uncertainty analysis, Alternating direction method of multipliers; Battery degradation; Charging energies; Charging strategies; Quadratic programming problems; Robust optimization; Scheduling problem; Uncertain parameters, Electric power transmission networks}, references={Xie, S., Qi, S., Lang, K., A data-driven power management strategy for plug-in hybrid electric vehicles including optimal battery depth of discharging (2020) IEEE Trans. Ind. Informat, 16 (5), pp. 3387-3396; Yilmaz, M., Krein, P.T., Review of battery charger topologies, charging power levels, and infrastructure for plug-in electric and hybrid vehicles (2013) IEEE Trans. Power Electron, 28 (5), pp. 2151-2169; Boenzi, F., Digiesi, S., Facchini, F., Mossa, G., Mummolo, G., Greening activities in warehouses: A model for identifying sustainable strategies in material handling (2015) Annals of DAAAM & Proceedings, 26 (1); Casalino, G., Del Buono, N., Mencar, C., Nonnegative matrix factorizations for intelligent data analysis (2016) Nonnegative Matrix Factorization Techniques. Signals and Communication Technology, , Naik G. (eds). Springer, Berlin, Heidelberg; D'Amato, G., Avitabile, G., Coviello, G., Talarico, C., Ddspll phase shifter architectures for phased arrays: Theory and techniques (2019) IEEE Access, 7, pp. 19461-19470; Turker, H., Bacha, S., Optimal minimization of plug-in electric vehicle charging cost with vehicle-to-home and vehicle-to-grid concepts (2018) IEEE Trans. Veh. Technol, 67 (11), pp. 10281-10292; Nafisi, H., Agah, S.M.M., Askarian Abyaneh, H., Abedi, M., Two-stage optimization method for energy loss minimization in microgrid based on smart power management scheme of phevs (2016) IEEE Trans. Smart Grid, 7 (3), pp. 1268-1276; Hosseini, S.M., Carli, R., Cavone, G., Dotoli, M., Distributed control of electric vehicle fleets considering grid congestion and battery degradation (2020) Internet Technology Letters, 2, p. e161; Liu, M., Phanivong, P.K., Shi, Y., Callaway, D.S., Decentralized charging control of electric vehicles in residential distribution networks (2019) IEEE Trans. Control Syst. Technol, 27 (1), pp. 266-281; Wang, P., Zou, S., Ma, Z., A partial augmented lagrangian method for decentralized electric vehicle charging in capacity-constrained distribution networks (2019) IEEE Access, 7, pp. 118229-118238; Carli, R., Dotoli, M., A decentralized control strategy for optimal charging of electric vehicle fleets with congestion management (2017) IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI), pp. 63-67. , Bari; Zhang, L., Kekatos, V., Giannakis, G.B., Scalable electric vehicle charging protocols (2017) IEEE Trans. Power Syst, 32 (2), pp. 1451-1462; Liu, S., Etemadi, A.H., A dynamic stochastic optimization for recharging plug-in electric vehicles (2018) IEEE Trans. Smart Grid, 9 (5), pp. 4154-4161; Wang, R., Xiao, G., Wang, P., Hybrid centralized-decentralized (hcd) charging control of electric vehicles (2017) IEEE Trans. Veh. Technol, 66 (8), pp. 6728-6741; Bertsimas, D., Brown, D.B., Caramanis, C., Theory and applications of robust optimization (2011) SIAM Review, 53 (3), pp. 464-501; Bertsimas, D., Sim, M., The price of robustness (2004) Operational Research, 52 (1), pp. 35-53; Deng, W., Lai, M.J., Peng, Z., Yin, W., Parallel multi-block admm with o (1/k) convergence (2017) Journal of Scientific Computing, 71 (2), pp. 712-736; Wang, J., Bharati, G.R., Paudyal, S., Ceylan, O., Bhattarai, B.P., Myers, K.S., Coordinated electric vehicle charging with reactive power support to distribution grids (2018) IEEE Trans. Ind. Inform, 15 (1), pp. 54-63; ISO-NE, Locational Marginal Prices, , http://www.iso-ne.com, accessed on 19 February 2020; Vagropoulos, S.I., Bakirtzis, A.G., Optimal bidding strategy for electric vehicle aggregators in electricity markets (2013) IEEE Trans. Power Syst, 28 (4), pp. 4031-4041}, document_type={Conference Paper}, source={Scopus}, }`

- Scarabaggio, P., La Scala, M., Carli, R. & Dotoli, M. (2020) Analyzing the Effects of COVID-19 Pandemic on the Energy Demand: The Case of Northern Italy IN 12th AEIT International Annual Conference, AEIT 2020..

[Bibtex]`@CONFERENCE{Scarabaggio2020, author={Scarabaggio, P. and La Scala, M. and Carli, R. and Dotoli, M.}, title={Analyzing the Effects of COVID-19 Pandemic on the Energy Demand: The Case of Northern Italy}, journal={12th AEIT International Annual Conference, AEIT 2020}, year={2020}, doi={10.23919/AEIT50178.2020.9241136}, art_number={9241136}, note={cited By 1}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097170993&doi=10.23919%2fAEIT50178.2020.9241136&partnerID=40&md5=a3abf95802bb66dc9a2d8715976cc126}, abstract={The COVID-19 crisis is profoundly influencing the global economic framework due to restrictive measures adopted by governments worldwide. Finding real-time data to correctly quantify this impact is very significant but not as straightforward. Nevertheless, an analysis of the power demand profiles provides insight into the overall economic trends. To accurately assess the change in energy consumption patterns, in this work we employ a multi-layer feed-forward neural network that calculates an estimation of the aggregated power demand in the north of Italy, (i.e, in one of the European areas that were most affected by the pandemics) in the absence of the COVID-19 emergency. After assessing the forecasting model reliability, we compare the estimation with the ground truth data to quantify the variation in power consumption. Moreover, we correlate this variation with the change in mobility behaviors during the lockdown period by employing the Google mobility report data. From this unexpected and unprecedented situation, we obtain some intuition regarding the power system macro-structure and its relation with the overall people's mobility. © 2020 AEIT.}, author_keywords={COVID-19; Lockdown; Machine learning; Neural networks; Power systems}, keywords={Electric power utilization; Energy utilization; Feedforward neural networks, Economic trends; Forecasting modeling; Global economics; Ground truth data; Macrostructures; Mobility behavior; Multilayer feedforward neural networks; Northern Italy, Multilayer neural networks}, references={Ferguson, N., Laydon, D., Nedjati-Gilani, G., Imai, N., Ainslie, K., Baguelin, M., Bhatia, S., Cuomo-Dannenburg, G., (2020) Impact of Non-Pharmaceutical Interventions to Reduce Covid-19 Mortality and Healthcare Demand, , Preprint at Spiral; Chontanawat, J., Hunt, L.C., Pierse, R., Does energy consumption cause economic growth?: Evidence from a systematic study of over 100 countries (2008) Journal of Policy Modeling, 30 (2), pp. 209-220; (2020) Terna.it., , www.terna.it, [Online]; Baldwin, R., Weder, B., (2020) Economics in the Time of COVID-19, , CEPR Press; Carli, R., Dotoli, M., Pellegrino, R., Multi-criteria decision-making for sustainable metropolitan cities assessment (2018) Journal of Environmental Management, 226, pp. 46-61; Abbasi, S., Abdi, H., Bruno, S., La Scala, M., Transmission network expansion planning considering load correlation using unscented transformation," int (2018) Journal of Electrical Power & Energy Systems, 103, pp. 12-20; Cicala, S., (2020) Early Economic Impacts of Covid-19 in Europe: A View from the Grid, , Tech. rep. University of Chicago, Tech. Rep; Narajewski, M., Ziel, F., (2020) Changes in Electricity Demand Pattern in Europe Due to Covid-19 Shutdowns, , Preprint; Ziel, F., Liu, B., Lasso estimation for gefcom2014 probabilistic electric load forecasting," int (2016) Journal of Forecasting, 32 (3), pp. 1029-1037; Alpaydin, E., (2020) Introduction to Machine Learning, , MIT press; Blonbou, R., Very short-term wind power forecasting with neural networks and adaptive bayesian learning (2011) Renewable Energy, 36 (3), pp. 1118-1124; Ogcu, G., Demirel, O.F., Zaim, S., Forecasting electricity consumption with neural networks and support vector regression (2012) Procedia-Social and Behavioral Sciences, 58, pp. 1576-1585; Hamedmoghadam, H., Joorabloo, N., Jalili, M., (2018) Australia's Long-Term Electricity Demand Forecasting Using Deep Neural Networks, , Preprint; Kuo, P.-H., Huang, C.-J., A high precision artificial neural networks model for short-term energy load forecasting (2018) Energies, 11 (1), p. 213; (2020) World Bank Open Data., , data.worldbank.org, [Online]; Openweathermap, , www.openweathermap.org, [Online]; Jain, A., Nandakumar, K., Ross, A., Score normalization in mul-timodal biometric systems (2005) Pattern Recognition, 38 (12), pp. 2270-2285; Payal, A., Rai, C., Reddy, B., Comparative analysis of bayesian reg-ularization and levenberg-marquardt training algorithm for localization in wireless sensor network 2013 15th Int. Conference on Advanced Communications Technology, 2013, pp. 191-194; Kong, W., Dong, Z.Y., Jia, Y., Hill, D.J., Xu, Y., Zhang, Y., Short-term residential load forecasting based on lstm recurrent neural network (2017) Ieee Trans. On Smart Grid, 10 (1), pp. 841-851; Covid-19 Effects, , www.bruegel.org/publications/datasets/bruegel-electricity-tracker-of-covid-19-lockdown-effects, [Online]; (2020) Covid-19 Community Mobility Reports, , www.google.com/covid19/mobility, Google llc, [Online]; Vehtari, A., Gelman, A., Gabry, J., Practical bayesian model evaluation using leave-one-out cross-validation and waic (2017) Statistics and Computing, 27 (5), pp. 1413-1432}, document_type={Conference Paper}, source={Scopus}, }`

- Carli, R. & Dotoli, M. (2020) A dynamic programming approach for the decentralized control of discrete optimizers with quadratic utilities and shared constraint IN 2020 28th Mediterranean Conference on Control and Automation, MED 2020., 611-616.

[Bibtex]`@CONFERENCE{Carli2020611, author={Carli, R. and Dotoli, M.}, title={A dynamic programming approach for the decentralized control of discrete optimizers with quadratic utilities and shared constraint}, journal={2020 28th Mediterranean Conference on Control and Automation, MED 2020}, year={2020}, pages={611-616}, doi={10.1109/MED48518.2020.9183012}, art_number={9183012}, note={cited By 0}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092158668&doi=10.1109%2fMED48518.2020.9183012&partnerID=40&md5=e3c2836d54651610b499a9686ac98fe7}, abstract={This paper addresses the problem of controlling a large set of agents, each with a quadratic utility function depending on individual combinatorial choices, and all sharing an affine constraint on available resources. Such a problem is formulated as an integer mono-constrained bounded quadratic knapsack problem. Differently from the centralized approaches typically proposed in the related literature, we present a new decentralized algorithm to solve the problem approximately in polynomial time by decomposing it into a finite series of sub-problems. We assume a minimal communication structure through the presence of a central coordinator that ensures the information exchange between agents. The proposed solution relies on a decentralized control algorithm that combines discrete dynamic programming with additive decomposition and value functions approximation. The optimality and complexity of the presented strategy are discussed, highlighting that the algorithm constitutes a fully polynomial approximation scheme. Numerical experiments are presented to show the effectiveness of the approach in the optimal resolution of large-scale instances. © 2020 IEEE.}, author_keywords={Decentralized optimization; Dynamic programming; Fully polynomial-time approximation scheme; Knapsack problem}, keywords={Approximation algorithms; Combinatorial optimization; Dynamic programming; Polynomial approximation, Additive decomposition; Centralized approaches; Communication structures; Decentralized algorithms; Information exchanges; Numerical experiments; Optimal resolution; Quadratic knapsack problems, Decentralized control}, references={Luh, P.B., Xiong, B., Chang, S.-C., Group elevator scheduling with advance information for normal and emergency modes (2008) Ieee Trans. Autom. Sci. Eng., 5 (2), pp. 245-258; Chu, C., Chu, F., Zhou, M., Chen, H., Shen, Q., A polynomial dynamic programming algorithm for crude oil transportation planning (2011) Ieee Trans. Autom. Sci. Eng., 9 (1), pp. 42-55; Cavone, G., Dotoli, M., Seatzu, C., Resource planning of intermodal terminals using timed petri nets (2016) 13th Intern. Workshop on Discrete Event Systems (WODES). Ieee, pp. 44-50; Vignali, R.M., Borghesan, F., Piroddi, L., Strelec, M., Prandini, M., Energy management of a building cooling system with thermal storage: An approximate dynamic programming solution (2017) Ieee Trans. Autom. Sci. Eng., 14 (2), pp. 619-633; Facchini, F., De Pascale, G., Faccilongo, N., Pallet picking strategy in food collecting center (2018) Applied Sciences, 8 (9), p. 1503; Bianchi, A., Pizzutilo, S., Vessio, G., An asm-based characterisation of starvation-free systems (2018) International Journal of Parallel, Emergent and Distributed Systems, 33 (1), pp. 35-51; Casalino, G., Castellano, G., Mencar, C., Data stream classification by dynamic incremental semi-supervised fuzzy clustering (2019) International Journal on Artificial Intelligence Tools, 28 (8), p. 1960009; Piccinni, G., Avitabile, G., Coviello, G., Talarico, C., Real-time distance evaluation system for wireless localization (2020) Ieee Transactions on Circuits and Systems I: Regular Papers; Kellerer, H., Pferschy, U., Pisinger, D., (2003) Knapsack Problems. Springer, , Berlin; Sniedovich, M., (2010) Dynamic Programming: Foundations and Principles, , CRC press; Bertsimas, D., Demir, R., An approximate dynamic programming approach to multidimensional knapsack problems (2002) Management Science, 48 (4), pp. 550-565; Rader, D.J., Jr., Woeginger, G.J., The quadratic 0-1 knapsack problem with series-parallel support (2002) Operations Research Letters, 30 (3), pp. 159-166; Fomeni, F.D., Letchford, A.N., A dynamic programming heuristic for the quadratic knapsack problem (2014) Informs Journal on Computing, 26 (1), pp. 173-182; Kellerer, H., Strusevich, V.A., Fully polynomial approximation schemes for a symmetric quadratic knapsack problem and its scheduling applications (2010) Algorithmica, 57 (4), pp. 769-795; Kataoka, S., Yamada, T., Dp-based algorithm and fptas for the knapsack sharing and related problems (2019) Journal of the Operations Research Society of Japan, 62 (1), pp. 1-14; Carli, R., Dotoli, M., A decentralized control strategy for energy retrofit planning of large-scale street lighting systems using dynamic programming (2017) 2017 13th Ieee Conference on Automation Science and Engineering (CASE). Ieee, pp. 1196-1200; Pisinger, D., The quadratic knapsack problem-A survey (2007) Discrete Applied Mathematics, 155 (5), pp. 623-648; Boyd, S., Vandenberghe, L., (2004) Convex Optimization. Cambridge University Press; Bertsekas, D.P., (1995) Dynamic Programming and Optimal Control, 1 (2). , Athena scientific Belmont, MA; Powell, W.B., (2007) Approximate Dynamic Programming: Solving the Curses of Dimensionality, 703. , John Wiley &Sons; https://www.ibm.com/support/knowledgecenter/en/SSSA5P12.6.2/ilog.odms.cplex.help/CPLEX/MATLAB/topics/gs.html, IBM, IBM ILOG CPLEX Optimization Studio Getting Started with CPLEX for MATLAB, accessed on 2019 Aug}, document_type={Conference Paper}, source={Scopus}, }`

- Cavone, G., Epicoco, N. & Dotoli, M. (2020) Process re-engineering based on colored petri nets: The case of an Italian textile company IN 2020 28th Mediterranean Conference on Control and Automation, MED 2020., 856-861.

[Bibtex]`@CONFERENCE{Cavone2020856, author={Cavone, G. and Epicoco, N. and Dotoli, M.}, title={Process re-engineering based on colored petri nets: The case of an Italian textile company}, journal={2020 28th Mediterranean Conference on Control and Automation, MED 2020}, year={2020}, pages={856-861}, doi={10.1109/MED48518.2020.9182937}, art_number={9182937}, note={cited By 0}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092143469&doi=10.1109%2fMED48518.2020.9182937&partnerID=40&md5=8c365f0bdb732312eac56e75db5e46d8}, abstract={Business process re-engineering is crucial for manufacturing companies to improve their productivity and efficiency. The identification of the main criticalities affecting the production processes and the implementation of effective re-engineering solutions can significantly reduce the company losses. However, such actions can be unsuccessful if suitable preliminary investigations on the effectiveness of the solutions are not performed. This paper proposes an integrated process re-engineering technique that allows to: identify workflows via the Unified Modeling Language; model and simulate the business process via Colored Petri Nets (CPNs); detect bottlenecks and waste sources through the Value Stream Mapping tool; rank the impact of the detected criticalities via a mathematical formulation of the Genba-Shikumi lean philosophy; simulate the re-engineering actions and evaluate their effectiveness using the CPN model. The aim is to offer an intuitive tool for strategic decision making, deployable at a managerial level in a digital twin approach. The proposed technique is tested on a textile company located in Southern Italy, showing its effectiveness in removing inefficiencies and ensuring the continuous improvement of the production process. © 2020 IEEE.}, author_keywords={Colored Petri Nets; Genba-Shikumi; Manufacturing; Process Re-engineering; Unified Modeling Language}, keywords={Computer hardware description languages; Criticality (nuclear fission); Decision making; Digital twin; Reengineering; Textiles; Unified Modeling Language, Business process re-engineering; Colored Petri Nets; Continuous improvements; Manufacturing companies; Mathematical formulation; Process reengineering; Strategic decision making; Value stream mapping, Petri nets}, references={Dooley, L., O'Sullivan, D., Decision support system for the management of systems change (1999) Technovation, 19 (8), pp. 483-493; Bhaskar, H., Singh, R., Business process reengineering: A recent review (2014) Global Journal of Business Management, 8 (2), pp. 24-51; Dassisti, M., Hy-change: A hybrid methodology for continuous performance improvement of manufacturing processes (2010) Int. J. Prod. Res., 48 (15), pp. 4397-4422; Nan, X., Li, A., You, J., Modeling and analysis of business process reengineering of manufacturing system (2008) 4th Int. Conf. Wireless Communications, Networking and Mobile Computing, pp. 1-4; Madushela, N., Pretorius, J.C., An integrated approach to business process reengineering management (2017) Proc. World Congress on Engineering 2017, 2, pp. 1-5; School, H.B., (2010) Improving Business Processes: Expert Solutions to Everyday Challenges, , Harvard Business Review Press; Ghanadbashi, S., Ramsin, R., Towards a method engineering approach for business process reengineering (2016) Iet Software, 10 (2), pp. 27-44; Guimaraes, T., Paranjape, K., Testing success factors for manufacturing bpr project phases (2013) Int. J. Adv. Manuf. Tech., 68 (9-12), pp. 1937-1947; Falcone, D., Di Bona, G., Silvestri, A., Forcina, A., Belfiore, G., Petrillo, A., An integrated model for an advanced production process-agile re-engineering project management (2018) IFAC-PapersOnLine, 51 (11), pp. 1630-1635; Vaez-Alaei, M., Baboli, A., Tavakkoli-Moghaddam, R., A new approach to integrate resilience engineering and business process reengineering design (2018) 2018 Ieee Int. Conf. Industrial Engineering and Engineering Management. Ieee, pp. 778-782; Dotoli, M., Epicoco, N., Falagario, M., Costantino, N., Turchiano, B., An integrated approach for warehouse analysis and optimization: A case study (2015) Comput. Ind., 70, pp. 56-69; Garcés, E.M., Mafla, G., Reyes, F., Analysis, review and development of a conceptual model, based on class diagrams as a component of uml, focused on industrial automation (2019) Int. J. Control Syst. and Robot., 4, pp. 6-10; Serrano Lasa, I., Ochoa Laburu, C., De Castro, V.R., An evaluation of the value stream mapping tool (2008) Business Process Management Journal, 14 (1), pp. 39-52; Dotoli, M., Epicoco, N., Falagario, M., Cavone, G., A timed petri nets model for intermodal freight transport terminals (2014) Ifac Proceedings Volumes, 47 (2), pp. 176-181; Aguilar-Saven, R.S., Business process modelling: Review and framework (2004) Int. J. Prod. Econ., 90 (2), pp. 129-149; Cavone, G., Dotoli, M., Epicoco, N., Seatzu, C., Intermodal terminal planning by petri nets and data envelopment analysis (2017) Control Eng. Pract., 69, pp. 9-22; Cavone, G., Dotoli, M., Epicoco, N., Franceschelli, M., Seatzu, C., Hybrid petri nets to re-design low-automated production processes: The case study of a sardinian bakery (2018) IFAC-PapersOnLine, 52 (7), pp. 265-270; Jensen, K., Monographs in theoretical computer science (1997) Coloured Petri Nets-Basic Concepts, 1. , Analysis Methods and Practical Use. Springer-Verlag; https://dreamprojectspa.it/en/, Dream Project accessed: 2020-04-28; Law, A., Kelton, W., (2010) Simulation Modeling and Analysis, , McGraw-Hill}, document_type={Conference Paper}, source={Scopus}, }`

- Scarabaggio, P., Carli, R., Cavone, G. & Dotoli, M. (2020) Smart control strategies for primary frequency regulation through electric vehicles: A battery degradation perspective. IN Energies, 13..

[Bibtex]`@ARTICLE{Scarabaggio2020, author={Scarabaggio, P. and Carli, R. and Cavone, G. and Dotoli, M.}, title={Smart control strategies for primary frequency regulation through electric vehicles: A battery degradation perspective}, journal={Energies}, year={2020}, volume={13}, number={17}, doi={10.3390/en13174586}, art_number={4586}, note={cited By 6}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090919511&doi=10.3390%2fen13174586&partnerID=40&md5=d7f07f0a819d149b5f1c143b707e731d}, abstract={Nowadays, due to the decreasing use of traditional generators in favor of renewable energy sources, power grids are facing a reduction of system inertia and primary frequency regulation capability. Such an issue is exacerbated by the continuously increasing number of electric vehicles (EVs), which results in enforcing novel approaches in the grid operations management. However, from being an issue, the increase of EVs may turn to be a solution to several power system challenges. In this context, a crucial role is played by the so-called vehicle-to-grid (V2G) mode of operation, which has the potential to provide ancillary services to the power grid, such as peak clipping, load shifting, and frequency regulation. More in detail, EVs have recently started to be effectively used for one of the most traditional frequency regulation approaches: the so-called frequency droop control (FDC). This is a primary frequency regulation, currently obtained by adjusting the active power of generators in the main grid. Because to the decommissioning of traditional power plants, EVs are thus recognized as particularly valuable solutions since they can respond to frequency deviation signals by charging or discharging their batteries. Against this background, we address frequency regulation of a power grid model including loads, traditional generators, and several EVs. The latter independently participate in the grid optimization process providing the grid with ancillary services, namely the FDC. We propose two novel control strategies for the optimal control of the batteries of EVs during the frequency regulation service. On the one hand, the control strategies ensure re-balancing the power and stabilizing the frequency of the main grid. On the other hand, the approaches are able to satisfy different types of needs of EVs during the charging process. Differently from the related literature, where the EVs perspective is generally oriented to achieve the optimal charge level, the proposed approaches aim at minimizing the degradation of battery devices. Finally, the proposed strategies are compared with other state-of-the-art V2G control approaches. The results of numerical experiments using a realistic power grid model show the effectiveness of the proposed strategies under the actual operating conditions. © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).}, author_keywords={Electric vehicle batteries (EVBs); Electric vehicles (EVs); Frequency droop control (FDC); Vehicle-to-grid (V2G)}, keywords={Automotive batteries; Charging (batteries); Electric control equipment; Electric generators; Electric network topology; Electric power transmission networks; Electric vehicles; Microgrids; Renewable energy resources; Vehicle-to-grid, Electric Vehicles (EVs); Frequency regulation services; Frequency regulations; Numerical experiments; Primary frequency regulation; Renewable energy source; Smart control strategies; Vehicle to Grid (V2G), Electric power system control}, references={Lawrenz, L., Xiong, B., Lorenz, L., Krumm, A., Hosenfeld, H., Burandt, T., Löffler, K., Von Hirschhausen, C., Exploring energy pathways for the low-carbon transformation in India-A model-based analysis (2018) Energies, 11, p. 3001; Oureilidis, K., Malamaki, K.N., Gallos, K., Tsitsimelis, A., Dikaiakos, C., Gkavanoudis, S., Cvetkovic, M., Ramos, J.L.M., Ancillary Services Market Design in Distribution Networks: Review and Identification of Barriers (2020) Energies, 13, p. 917; Eto, J.H., Berkeley, L., Undrill, J., Mackin, P., Daschmans, R., Williams, B., Haney, B., Illian, H., (2010) Use of Frequency Response Metrics to Assess the Planning and Operating Requirements for Reliable Integration of Variable Renewable Generation, , Lawrence Berkeley National Laboratory (LBNL): Berkeley, CA, USA; Peng, C., Zou, J., Lian, L., Dispatching strategies of electric vehicles participating in frequency regulation on power grid: A review (2017) Renew. Sustain. Energy Rev, 68, pp. 147-152; Alsharafi, A.S., Besheer, A.H., Emara, H.M., Primary frequency response enhancement for future low inertia power systems using hybrid control technique (2018) Energies, 11, p. 699; Adrees, A., Papadopoulos, P.N., Milanovic, J.V., A framework to assess the effect of reduction in inertia on system frequency response (2016) Proceedings of the 2016 IEEE Power and Energy Society General Meeting (PESGM), pp. 1-5. , Boston, MA, USA, 17-21 July IEEE: Piscataway, NJ, USA, 2016; (2020), https://www.terna.it/en, Terna. (accessed on 16 July 2020); Lucchese, F.C., Canha, L.N., Brignol, W.S., Hammerschmitt, B.K., Da Silva, L.N., Martins, C.C., Energy Storage Systems Role in Supporting Renewable Resources: Global Overview (2019) Proceedings of the 2019 54th International Universities Power Engineering Conference (UPEC), pp. 1-6. , Bucharest, Romania, 3-6 September IEEE: Piscataway, NJ, USA, 2019; Zeh, A., Müller, M., Naumann, M., Hesse, H.C., Jossen, A., Witzmann, R., Fundamentals of using battery energy storage systems to provide primary control reserves in Germany (2016) Batteries, 2, p. 29; Bellocchi, S., Manno, M., Noussan, M., Vellini, M., Impact of grid-scale electricity storage and electric vehicles on renewable energy penetration: A case study for Italy (2019) Energies, 12, p. 1303; Hosseini, S.M., Carli, R., Cavone, G., Dotoli, M., Distributed control of electric vehicle fleets considering grid congestion and battery degradation (2020) Internet Technol. Lett, 3, p. e161; Boenzi, F., Digiesi, S., Facchini, F., Mossa, G., Mummolo, G., Sustainable warehouse logistics: A NIP model for non-road vehicles and storage configuration selection (2015) Proceedings of the XX Summer School Operational Excellence Experience “Francesco Turco”, , Naples, Italy, 16-18 September; Casalino, G., Del Buono, N., Mencar, C., Nonnegative matrix factorizations for intelligent data analysis (2016) Non-Negative Matrix Factorization Techniques, pp. 49-74. , Springer: Berlin/Heidelberg, Germany; D'Amato, G., Avitabile, G., Coviello, G., Talarico, C., DDS-PLL phase shifter architectures for phased arrays: Theory and techniques (2019) IEEE Access, 7, pp. 19461-19470; Kotb, A.O., Shen, Y.C., Zhu, X., Huang, Y., iParker-A new smart car-parking system based on dynamic resource allocation and pricing (2016) IEEE Trans. Intell. Transp. Syst, 17, pp. 2637-2647; Hosseini, S.S., Badri, A., Parvania, M., The plug-in electric vehicles for power system applications: The vehicle to grid (V2G) concept (2012) Proceedings of the 2012 IEEE International Energy Conference and Exhibition (ENERGYCON), pp. 1101-1106. , Florence, Italy, 9-12 September IEEE: Piscataway, NJ, USA, 2012; Galus, M.D., Koch, S., Andersson, G., Provision of load frequency control by PHEVs, controllable loads, and a cogeneration unit (2011) IEEE Trans. Ind. Electron, 58, pp. 4568-4582; Datta, U., Kalam, A., Shi, J., Battery Energy Storage System for Aggregated Inertia-Droop Control and a Novel Frequency Dependent State-of-Charge Recovery (2020) Energies, 13, p. 2003; Kempton, W., Udo, V., Huber, K., Komara, K., Letendre, S., Baker, S., Brunner, D., Pearre, N., A test of vehicle-to-grid (V2G) for energy storage and frequency regulation in the PJM system (2008) Results Ind. Univ. Res. Partnersh, 32; Jha, I., Sen, S., Tiwari, M., Singh, M.K., Control strategy for Frequency Regulation using Battery Energy Storage with optimal utilization (2014) Proceedings of the 2014 IEEE 6th India International Conference on Power Electronics (IICPE), pp. 1-4. , Kurukshetra, India, 8-10 December IEEE: Piscataway, NJ, USA, 2014; Lopes, J.A.P., Soares, F.J., Almeida, P.M.R., Integration of electric vehicles in the electric power system (2010) Proc. IEEE, 99, pp. 168-183; Brooks, A., Lu, E., Reicher, D., Spirakis, C., Weihl, B., Demand dispatch (2010) IEEE Power Energy Mag, 8, pp. 20-29; Oudalov, A., Chartouni, D., Ohler, C., Optimizing a battery energy storage system for primary frequency control (2007) IEEE Trans. Power Syst, 22, pp. 1259-1266; Peng, C., Zou, J., Lian, L., Li, L., An optimal dispatching strategy for V2G aggregator participating in supplementary frequency regulation considering EV driving demand and aggregator's benefits (2017) Appl. Energy, 190, pp. 591-599; Pillai, J.R., Bak-Jensen, B., Integration of vehicle-to-grid in the western Danish power system (2011) IEEE Trans. Sustain. Energy, 2, pp. 12-19; Masuta, T., Yokoyama, A., Supplementary load frequency control by use of a number of both electric vehicles and heat pump water heaters (2012) IEEE Trans. Smart Grid, 3, pp. 1253-1262; Liu, H., Hu, Z., Song, Y., Lin, J., Decentralized vehicle-to-grid control for primary frequency regulation considering charging demands (2013) IEEE Trans. Power Syst, 28, pp. 3480-3489; Almeida, P.R., Lopes, J.P., Soares, F., Seca, L., Electric vehicles participating in frequency control: Operating islanded systems with large penetration of renewable power sources (2011) Proceedings of the 2011 IEEE Trondheim PowerTech, pp. 1-6. , Trondheim, Norway, 19-23 June IEEE: Piscataway, NJ, USA, 2011; Yang, J., Zeng, Z., Tang, Y., Yan, J., He, H., Wu, Y., Load frequency control in isolated micro-grids with electrical vehicles based on multivariable generalized predictive theory (2015) Energies, 8, pp. 2145-2164; Li, X., Huang, Y., Huang, J., Tan, S., Wang, M., Xu, T., Cheng, X., Modeling and control strategy of battery energy storage system for primary frequency regulation (2014) Proceedings of the 2014 International Conference on Power System Technology, pp. 543-549. , Chengdu, China, 20-22 October IEEE: Piscataway, NJ, USA, 2014; Yang, J.S., Choi, J.Y., An, G.H., Choi, Y.J., Kim, M.H., Won, D.J., Optimal scheduling and real-time state-of-charge management of energy storage system for frequency regulation (2016) Energies, 9, p. 1010; Ota, Y., Taniguchi, H., Nakajima, T., Liyanage, K.M., Baba, J., Yokoyama, A., Autonomous distributed V2G (vehicle-to-grid) satisfying scheduled charging (2011) IEEE Trans. Smart Grid, 3, pp. 559-564; Hernández, J.C., Sanchez-Sutil, F., Vidal, P., Rus-Casas, C., Primary frequency control and dynamic grid support for vehicle-to-grid in transmission systems (2018) Int. J. Electr. Power Energy Syst, 100, pp. 152-166; Vahedipour-Dahraie, M., Rashidizaheh-Kermani, H., Najafi, H.R., Anvari-Moghaddam, A., Guerrero, J.M., Coordination of EVs participation for load frequency control in isolated microgrids (2017) Appl. Sci, 7, p. 539; Khooban, M.H., Niknam, T., Blaabjerg, F., Dragičević, T., A new load frequency control strategy for micro-grids with considering electrical vehicles (2017) Electr. Power Syst. Res, 143, pp. 585-598; Cam, E., Gorel, G., Mamur, H., Use of the genetic algorithm-based fuzzy logic controller for load-frequency control in a two area interconnected power system (2017) Appl. Sci, 7, p. 308; Tchagang, A., Yoo, Y., V2B/V2G on Energy Cost and Battery Degradation under Different Driving Scenarios, Peak Shaving, and Frequency Regulations (2020) World Electr. Veh. J, 11, p. 14; Baure, G., Dubarry, M., Durability and Reliability of EV Batteries under Electric Utility Grid Operations: Impact of Frequency Regulation Usage on Cell Degradation (2020) Energies, 13, p. 2494; Stroe, D.I., Lærke, R., Stan, A.I., Kjær, P.C., Teodorescu, R., Kær, S.K., Field experience from Li-ion BESS delivering primary frequency regulation in the Danish energy market (2014) Ecs Trans, 61, p. 1. , Swierczy nski, M; Stroe, D.I., Knap, V., Swierczynski, M., Stroe, A.I., Teodorescu, R., Operation of a grid-connected lithium-ion battery energy storage system for primary frequency regulation: A battery lifetime perspective (2016) IEEE Trans. Ind. Appl, 53, pp. 430-438; Han, S., Han, S., Economic feasibility of V2G frequency regulation in consideration of battery wear (2013) Energies, 6, pp. 748-765; Kundur, P., Balu, N.J., Lauby, M.G., (1994) Power System Stability and Control, 7. , McGraw-Hill: New York, NY, USA; Maheshwari, A., Paterakis, N.G., Santarelli, M., Gibescu, M., Optimizing the operation of energy storage using a non-linear lithium-ion battery degradation model (2020) Appl. Energy, 261, p. 114360; Yan, G., Liu, D., Li, J., Mu, G., A cost accounting method of the Li-ion battery energy storage system for frequency regulation considering the effect of life degradation (2018) Prot. Control Mod. Power Syst, 3, pp. 1-9; Yan, G., Zhu, X., Li, J., Mu, G., Luo, W., Yang, K., Control strategy design for hybrid energy storage system with intrinsic operation life measurement and calculation (2013) Dianli Xitong Zidonghua Automation Electr. Power Syst, 37, pp. 110-114}, document_type={Article}, source={Scopus}, }`

- Scarabaggio, P., Carli, R. & Dotoli, M. (2020) A game-theoretic control approach for the optimal energy storage under power flow constraints in distribution networks IN IEEE International Conference on Automation Science and Engineering., 1281-1286.

[Bibtex]`@CONFERENCE{Scarabaggio20201281, author={Scarabaggio, P. and Carli, R. and Dotoli, M.}, title={A game-theoretic control approach for the optimal energy storage under power flow constraints in distribution networks}, journal={IEEE International Conference on Automation Science and Engineering}, year={2020}, volume={2020-August}, pages={1281-1286}, doi={10.1109/CASE48305.2020.9216800}, art_number={9216800}, note={cited By 1}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85094128798&doi=10.1109%2fCASE48305.2020.9216800&partnerID=40&md5=e4802c482f96ac90afd6c7a2c7f2b199}, abstract={Traditionally, the management of power distribution networks relies on the centralized implementation of the optimal power flow and, in particular, the minimization of the generation cost and transmission losses. Nevertheless, the increasing penetration of both renewable energy sources and independent players such as ancillary service providers in modern networks have made this centralized framework inadequate. Against this background, we propose a noncooperative game-theoretic framework for optimally controlling energy storage systems (ESSs) in power distribution networks. Specifically, in this paper we address a power grid model that comprehends traditional loads, distributed generation sources and several independent energy storage providers, each owning an individual ESS. Through a rolling-horizon approach, the latter participate in the grid optimization process, aiming both at increasing the penetration of distributed generation and leveling the power injection from the transmission grid. Our framework incorporates not only economic factors but also grid stability aspects, including the power flow constraints. The paper fully describes the distribution grid model as well as the underlying market hypotheses and policies needed to force the energy storage providers to find a feasible equilibrium for the network. Numerical experiments based on the IEEE 33-bus system confirm the effectiveness and resiliency of the proposed framework. © 2020 IEEE.}, keywords={Data storage equipment; Distributed power generation; Electric load flow; Electric network analysis; Electric power system economics; Electric power transmission; Energy storage; Game theory; Renewable energy resources; Storage as a service (STaaS), Distributed generation source; Energy Storage Systems (ESSs); Noncooperative game; Numerical experiments; Optimal power flows; Power distribution network; Renewable energy source; Transmission grids, Electric power transmission networks}, references={Fang, X., Misra, S., Xue, G., Yang, D., Smart grid-the new and improved power grid: A survey (2011) Ieee Communications Surveys & Tutorials, 14 (4), pp. 944-980; Carli, R., Dotoli, M., Decentralized control for residential energy management of a smart users' microgrid with renewable energy exchange (2019) IEEE/CAA Journal of Automatica Sinica, 6 (3), pp. 641-656; Rodrigues, E., Godina, R., Santos, S.F., Bizuayehu, A.W., Contreras, J., Catalão, J., Energy storage systems supporting increased penetration of renewables in islanded systems (2014) Energy, 75, pp. 265-280; Bahrami, S., Amini, M.H., Shafie-Khah, M., Catalao, J.P., A decentralized renewable generation management and demand response in power distribution networks (2018) Ieee Trans. Sustain. Energy, 9 (4), pp. 1783-1797; Dörfler, F., Bolognani, S., Simpson-Porco, J.W., Grammatico, S., Distributed control and optimization for autonomous power grids (2019) 2019 18th European Control Conference (ECC), pp. 2436-2453; Hübner, N., Rink, Y., Suriyah, M., Leibfried, T., (2019) Distributed Ac-Dc Optimal Power Flow in the European Transmission Grid with Admm, , arXiv preprint; Yang, L., Luo, J., Xu, Y., Zhang, Z., Dong, Z., A distributed dual consensus admm based on partition for dc-dopf with carbon emission trading (2019) Ieee Trans. Ind. Informat; Atzeni, I., Ordóñez, L.G., Scutari, G., Palomar, D.P., Fonol-Losa, J.R., Demand-side management via distributed energy generation and storage optimization (2012) Ieee Trans. Smart Grid, 4 (2), pp. 866-876; Chen, J., Zhu, Q., A game-theoretic framework for resilient and distributed generation control of renewable energies in microgrids (2016) Ieee Trans. Smart Grid, 8 (1), pp. 285-295; Carli, R., Dotoli, M., Palmisano, V., A distributed control approach based on game theory for the optimal energy scheduling of a residential microgrid with shared generation and storage (2019) 2019 Ieee 15th International Conference on Automation Science and Engineering (CASE), pp. 960-965; Wu, D., Yang, T., Stoorvogel, A.A., Stoustrup, J., Distributed optimal coordination for distributed energy resources in power systems (2016) Ieee Trans. Autom. Sci. Eng, 14 (2), pp. 414-424; Kargarian, A., Mohammadi, J., Guo, J., Chakrabarti, S., Barati, M., Hug, G., Kar, S., Baldick, R., Toward distributed/decentralized dc optimal power flow implementation in future electric power systems (2016) Ieee Trans. Smart Grid, 9 (4), pp. 2574-2594; Kisacikoglu, M.C., Kesler, M., Tolbert, L.M., Single-phase onboard bidirectional pev charger for v2g reactive power operation (2014) Ieee Trans. Smart Grid, 6 (2), pp. 767-775; Maskar, M.B., Thorat, A., Korachgaon, I., A review on optimal power flow problem and solution methodologies (2017) 2017 International Conference on Data Management, Analytics and Innovation (ICDMAI), pp. 64-70; Scutari, G., Palomar, D.P., Facchinei, F., Pang, J.-S., Convex optimization, game theory, and variational inequality theory (2010) Ieee Signal Process. Mag., 27 (3), pp. 35-49; Belgioioso, G., Grammatico, S., Projected-gradient algorithms for generalized equilibrium seeking in aggregative games arepre-conditioned forward-backward methods (2018) 2018 European Control Conference (ECC), pp. 2188-2193; Scarabaggio, P., Carli, R., Dotoli, M., (2020) Experiments Dataset, , https://sites.google.com/view/storagecontrol, [Online]; Vinkovic, A., Mihalic, R., A current-based model of an ipfc for newton-raphson power flow (2009) Electr. Pow. Syst. Res., 79 (8), pp. 1247-1254}, document_type={Conference Paper}, source={Scopus}, }`

- Carli, R., Cavone, G., Pippia, T., Schutter, B. D. & Dotoli, M. (2020) A Robust MPC Energy Scheduling Strategy for Multi-Carrier Microgrids IN IEEE International Conference on Automation Science and Engineering., 152-158.

[Bibtex]`@CONFERENCE{Carli2020152, author={Carli, R. and Cavone, G. and Pippia, T. and Schutter, B.D. and Dotoli, M.}, title={A Robust MPC Energy Scheduling Strategy for Multi-Carrier Microgrids}, journal={IEEE International Conference on Automation Science and Engineering}, year={2020}, volume={2020-August}, pages={152-158}, doi={10.1109/CASE48305.2020.9216875}, art_number={9216875}, note={cited By 3}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85094114652&doi=10.1109%2fCASE48305.2020.9216875&partnerID=40&md5=92ef257518791ef22242636d1c989285}, abstract={We present a Robust Model Predictive Control (RMPC) approach for multi-carrier microgrids, i.e., microgrids based on gas and electricity. The microgrid that we consider includes thermal loads, electrical loads, renewable energy sources, energy storage systems, heat pumps, and combined heat and power plants. Moreover, the system under control is affected by several external disturbances, e.g., uncertainty in renewable energy generation, electrical and thermal demand. The goal of the controller is to minimize the overall economical cost and the energy exchange with the main grid, while guaranteeing comfort. Whereas several RMPC methods have been developed for electrical or thermal microgrids, little or no attention has been devoted to robust control of multi-carrier microgrids. Therefore, we consider a novel RMPC algorithm that can improve the performance with respect to classical deterministic Model Predictive Control (Det-MPC) controllers in the context of multi-carrier microgrids. The RMPC method relies on the box-uncertainty-set robust optimization, where uncertain parameters are assumed to take their values from different intervals independently. The RMPC approach is able to successfully satisfy the constraints even in the presence of the mentioned disturbances. Simulations of a realistic residential case study show the benefits of the proposed approach with respect to Det-MPC controllers. © 2020 IEEE.}, author_keywords={Energy and Environment-Aware Automation; Microgrid; Optimization and Optimal Control; Robust Model Predictive Control; Set-based Uncertainty}, keywords={Cogeneration plants; Electric energy storage; Electric loads; Microgrids; Model predictive control; Optimization; Predictive control systems; Renewable energy resources; Robust control; Uncertainty analysis, Deterministic modeling; Energy storage systems; External disturbances; Renewable energy generation; Renewable energy source; Robust model predictive controls (RMPC); Scheduling strategies; Uncertain parameters, Controllers}, references={Fang, X., Misra, S., Xue, G., Yang, D., Smart grid-the new and improved power grid: A survey (2012) Ieee Commun. Surveys Tuts, 14 (4), pp. 944-980; Hirsch, A., Parag, Y., Guerrero, J., Microgrids: A review of technologies key drivers, and outstanding issues (2018) Renew. Sust. Energ. Rev., 90, pp. 402-411; Carli, R., Dotoli, M., Jantzen, J., Kristensen, M., Ben Othman, S., Energy scheduling of a smart microgrid with shared photovoltaic panels and storage: The case of the ballen marina in samsø (2020) Energy, 198, p. 117188; Camacho, E.F., Alba, C.B., Model predictive control (2013) Advanced Textbooks in Control and Signal Processing, , Springer London; Mayne, D.Q., Model predictive control: Recent developments and future promise (2014) Automatica, 50 (12), pp. 2967-2986; Bittel, H., Jones, C.N., Parisio, A., Use of model predictive control for short-term operating reserve using commercial buildings in the united kingdom context (2018) 2018 Ieee Decis. Contr., pp. 7308-7313; Parisio, A., Rikos, E., Glielmo, L., A model predictive control approach to microgrid operation optimization (2014) Ieee Trans. Control Syst. Technol., 22 (5), pp. 1813-1827; Verrilli, F., Srinivasan, S., Gambino, G., Canelli, M., Himanka, M., Del Vecchio, C., Sasso, M., Glielmo, L., Model predictive control-based optimal operations of district heating system with thermal energy storage and flexible loads (2017) Ieee Trans. Autom. Sci. Eng., 14 (2), pp. 547-557; Parisio, A., Rikos, E., Glielmo, L., Stochastic model predictive control for economic/environmental operation management of microgrids: An experimental case study (2016) J. Process Contr., 43, pp. 24-37; Liberati, F., Di Giorgio, A., Giuseppi, A., Pietrabissa, A., Habib, E., Martirano, L., Joint model predictive control of electric and heating resources in a smart building (2019) Ieee Trans. Ind Appl., 55 (6), pp. 7015-7027; Alavi, F., Park Lee, E., Wouw De, N.Van, De Schutter, B., Lukszo, Z., Fuel cell cars in a microgrid for synergies between hydrogen and electricity networks (2017) Appl. Energ., 192, pp. 296-304; Pereira, M., Peña De La, D.Muñoz, Limon, D., Robust economic model predictive control of a community micro-grid (2017) Renew. Energ., 100, pp. 3-17. , Special Issue: Control and Optimization of Renewable Energy Systems; Zhang, Y., Fu, L., Zhu, W., Bao, X., Liu, C., Robust model predictive control for optimal energy management of island microgrids with uncertainties (2018) Energy, 164, pp. 1229-1241; Zhai, M., Liu, Y., Zhang, T., Zhang, Y., Robust model predictive control for energy management of isolated microgrids (2017) 2017 Ieee In. C. Ind. Eng. Eng. Man., pp. 2049-2053; Bertsimas, D., Sim, M., The price of robustness (2004) Oper. Res., 52 (1), pp. 35-53; Bertsimas, D., Brown, D.B., Caramanis, C., Theory and applications of robust optimization (2011) Siam Review, 53 (3), pp. 464-501; Soyster, A.L., Convex programming with set-inclusive constraints and applications to inexact linear programming (1973) Oper. Res., 21 (5), pp. 1154-1157; Neon Neue Energieökonomik, , https://data.open-power-system-data.org, Technical University of Berlin, eth Zürich, and diw Berlin. Open-power-system-data; Emhires Dataset Solarpower, , https://setis.ec.europa.eu/publications/relevant-reports/emhires-dataset-part-ii-solarpower-generation, European Commission setis; Mekh & vaasaett energie-control Energy Price Index, , https://www.energypriceindex.com/latest-update; (2016) Gurobi Optimization, , https://www.gurobi.com, Inc. Gurobi optimizer reference manual}, document_type={Conference Paper}, source={Scopus}, }`

- Dotoli, M. & Jia, Q. -S. (2020) Guest Editorial Special Section on the 2017 International Conference on Automation Science and Engineering. IN IEEE Transactions on Automation Science and Engineering, 17.1095-1096.

[Bibtex]`@ARTICLE{Dotoli20201095, author={Dotoli, M. and Jia, Q.-S.}, title={Guest Editorial Special Section on the 2017 International Conference on Automation Science and Engineering}, journal={IEEE Transactions on Automation Science and Engineering}, year={2020}, volume={17}, number={3}, pages={1095-1096}, doi={10.1109/TASE.2020.2990785}, art_number={9131120}, note={cited By 0}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087616647&doi=10.1109%2fTASE.2020.2990785&partnerID=40&md5=d74ffa09fbe26897b73ee82716e7f402}, document_type={Editorial}, source={Scopus}, }`

- Cavone, G., Dotoli, M., Epicoco, N., Morelli, D. & Seatzu, C. (2020) Design of Modern Supply Chain Networks Using Fuzzy Bargaining Game and Data Envelopment Analysis. IN IEEE Transactions on Automation Science and Engineering, 17.1221-1236.

[Bibtex]`@ARTICLE{Cavone20201221, author={Cavone, G. and Dotoli, M. and Epicoco, N. and Morelli, D. and Seatzu, C.}, title={Design of Modern Supply Chain Networks Using Fuzzy Bargaining Game and Data Envelopment Analysis}, journal={IEEE Transactions on Automation Science and Engineering}, year={2020}, volume={17}, number={3}, pages={1221-1236}, doi={10.1109/TASE.2020.2977452}, art_number={9040428}, note={cited By 5}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087545403&doi=10.1109%2fTASE.2020.2977452&partnerID=40&md5=36c9b0b7af0bb44437e25bed1ffbd281}, abstract={This article proposes a novel methodology for multistage, multiproduct, multi-item, and closed-loop Supply Chain Network (SCN) design under uncertainty. The method considers that multiple products are manufactured by the SCN, each composed by multiple items, and that some of the sold products may require repair, refurbishing, or remanufacturing activities. We solve the two main decisions that take place in the medium-/short-term planning horizon, namely partners' selection and allocation of the received orders among them. The partners' selection problem is solved by a cross-efficiency fuzzy Data Envelopment Analysis technique, which allows evaluating the efficiency of each SCN member and ranking them against multiple conflicting objectives under uncertain data on their performance. Then, according to the estimated customers' demand, the order allocation problem is solved by a fuzzy bargaining game problem, where each SCN actor behaves to simultaneously maximize both its own profit and the service level of the overall SCN in terms of efficiency, costs, and lead time. An illustrative example from the literature is finally presented. Note to Practitioners-We present a decision tool to address the optimal design, performance evaluation, and continuous improvement of modern cooperative SCNs. We propose an effective method to jointly solve the members' selection and the orders' allocation, considering the complex structure of modern SCNs, the multiobjective nature of the problems, and the uncertainty characterizing economic markets. Competition within SCNs stages and cooperation along the chain are considered, with the aim to improve both financial and environmental sustainability, while ensuring the highest service levels to customers. © 2004-2012 IEEE.}, author_keywords={Bargaining game; fuzzy set theory; order allocation; supplier selection; Supply Chain Network Design (SCND)}, keywords={Data envelopment analysis; Efficiency; Supply chains; Sustainable development, Closed-loop supply chain networks; Conflicting objectives; Continuous improvements; Design under uncertainty; Environmental sustainability; Fuzzy data envelopment analysis; Selection problems; Supply chain network, Game theory}, references={Choi, T.-M., Supply chain systems coordination with multiple risk sensitive retail buyers (2016) IEEE Trans. Syst., Man, Cybern., Syst., 46 (5), pp. 636-645. , May; Zhou, Y., Gong, D.-C., Huang, B., Peters, B.A., The impacts of carbon tariff on green supply chain design (2017) IEEE Trans. Autom. Sci. Eng., 14 (3), pp. 1542-1555. , Jul; Dias, L.S., Ierapetritou, M.G., From process control to supply chain management: An overview of integrated decision making strategies (2017) Comput. Chem. Eng., 106, pp. 826-835. , Nov; Guo, Y., Hu, F., Allaoui, H., Boulaksil, Y., A distributed approximation approach for solving the sustainable supply chain network design problem (2018) Int. J. Prod. Res., 57 (11), pp. 3695-3718. , Dec; Dubey, R., Gunasekaran, A., Childe, S.J., Papadopoulos, T., Blome, C., Luo, Z., Antecedents of resilient supply chains: An empirical study (2019) IEEE Trans. Eng. Manag., 66 (1), pp. 8-19. , Feb; Dotoli, M., Epicoco, N., Falagario, M., Integrated supplier selection and order allocation under uncertainty in agile supply chains (2015) Proc. IEEE 20th Conf. Emerg. Technol. Factory Autom. (ETFA), pp. 1-6. , Sep; Matopoulos, A., Barros, A.C., Vorst Der Van J. A., J.G., Resource-efficient supply chains: A research framework, literature review and research agenda (2015) Supply Chain Manage., Int. J., 20 (2), pp. 218-236. , Mar; Dotoli, M., Epicoco, N., Falagario, M., A fuzzy technique for supply chain network design with quantity discounts (2016) Int. J. Prod. Res., 55 (7), pp. 1862-1884. , Apr; Dotoli, M., Epicoco, N., Integrated network design of agile resource-efficient supply chains under uncertainty IEEE Trans. Syst., Man, Cybern., Syst, , to be published; Cavone, G., Dotoli, M., Epicoco, N., Morelli, D., Seatzu, C., A game-theoretical design technique for multi-stage supply chains under uncertainty (2018) Proc. IEEE 14th Int. Conf. Autom. Sci. Eng. (CASE), pp. 528-533. , Aug; Govindan, K., Fattahi, M., Keyvanshokooh, E., Supply chain network design under uncertainty: A comprehensive review and future research directions (2017) Eur. J. Oper. Res., 263 (1), pp. 108-141. , Nov; Kumar, D., Rahman, Z., Chan, F.T.S., A fuzzy AHP and fuzzy multi-objective linear programming model for order allocation in a sustainable supply chain: A case study (2016) Int. J. Comput. Integr. Manuf., 30 (6), pp. 535-551. , Feb; Srivathsan, S., Kamath, M., An analytical performance modeling approach for supply chain networks (2012) IEEE Trans. Autom. Sci. Eng., 9 (2), pp. 265-275. , Apr; Benjaafar, S., Li, Y., Daskin, M., Carbon footprint and the management of supply chains: Insights from simple models (2013) IEEE Trans. Autom. Sci. Eng., 10 (1), pp. 99-116. , Jan; Dotoli, M., Epicoco, N., Falagario, M., Sciancalepore, F., A cross-efficiency fuzzy data envelopment analysis technique for performance evaluation of decision making units under uncertainty (2015) Comput. Ind. Eng., 79, pp. 103-114. , Jan; Sevilla, N.C., Padon, M.L.A., Cabusas, K.G.L., Ocampo, L.A., Abad, G.K.M., Recent approaches to supplier selection: A review of literature within 2006-2016 (2018) Int. J. Integr. Supply Manage., 12 (1-2), p. 22; Tsao, Y.-C., Zhang, Q., Zeng, Q., Supply chain network design considering RFID adoption (2017) IEEE Trans. Autom. Sci. Eng., 14 (2), pp. 977-983. , Apr; Alikhani, R., Torabi, S.A., Altay, N., Strategic supplier selection under sustainability and risk criteria (2019) Int. J. Prod. Econ., 208, pp. 69-82. , Feb; Lima-Junior, F.R., Carpinetti, L.C.R., Quantitative models for supply chain performance evaluation: A literature review (2017) Comput. Ind. Eng., 113, pp. 333-346. , Nov; Dotoli, M., Epicoco, N., Falagario, M., Sciancalepore, F., A stochastic cross-efficiency data envelopment analysis approach for supplier selection under uncertainty (2015) Int. Trans. Oper. Res., 23 (4), pp. 725-748. , Feb; Salwa, C.H., Ramanan, T.R., Development of a sustainable strategic marketing model for self-help groups-An analytical hierarchical approach (2017) Int. J. Services Oper. Manage., 26 (3), pp. 318-331; Soheilirad, S., Govindan, K., Mardani, A., Zavadskas, E.K., Nilashi, M., Zakuan, N., Application of data envelopment analysis models in supply chain management: A systematic review and meta-analysis (2017) Ann. Oper. Res., 271 (2), pp. 915-969. , Sep; Ghorabaee, M.K., Amiri, M., Zavadskas, E.K., Antucheviciene, J., Supplier evaluation and selection in fuzzy environments: A review of MADM approaches (2017) Econ. Res.-Ekonomska Istraẑivanja, 30 (1), pp. 1073-1118. , May; Simić, D., Kovacević, I., Svircević, V., Simić, S., 50 years of fuzzy set theory and models for supplier assessment and selection: A literature review (2017) J. Appl. Log., 24, pp. 85-96. , Nov; Azadnia, A.H., Saman, M.Z.M., Wong, K.Y., Sustainable supplier selection and order lot-sizing: An integrated multi-objective decision-making process (2014) Int. J. Prod. Res., 53 (2), pp. 383-408. , Jul; Sadic, S., De Sousa, J.P., Crispim, J.A., A two-phase MILP approach to integrate order, customer and manufacturer characteristics into dynamic manufacturing network formation and operational planning (2018) Expert Syst. Appl., 96, pp. 462-478. , Apr; Leider, S., Lovejoy, W.S., Bargaining in supply chains (2016) Manage. Sci., 62 (10), pp. 3039-3058. , Sep; Esmaeili, M., Allameh, G., Tajvidi, T., Using game theory for analysing pricing models in closed-loop supply chain from short-and long-term perspectives (2015) Int. J. Prod. Res., 54 (7), pp. 2152-2169. , Nov; Xu, J., Zhao, S., Noncooperative game-based equilibrium strategy to address the conflict between a construction company and selected suppliers (2017) J. Construct. Eng. Manage., 143 (8). , Aug; Mohammaditabar, D., Ghodsypour, S.H., Hafezalkotob, A., A game theoretic analysis in capacity-constrained supplier-selection and cooperation by considering the total supply chain inventory costs (2016) Int. J. Prod. Econ., 181, pp. 87-97. , Nov; Nagarajan, M., Soŝić, G., Game-theoretic analysis of cooperation among supply chain agents: Review and extensions (2008) Eur. J. Oper. Res., 187 (3), pp. 719-745. , Jun; Leng, M., Parlar, M., Game theoretic applications in supply chain management: A review (2016) Inf. Syst. Oper. Res., 43 (3), pp. 187-220. , May; Zimmermann, H.-J., Fuzzy set theory (2010) Wiley Interdiscipl. Reviews: Comput. Statist., 2 (3), pp. 317-332. , Apr; Charnes, A., Cooper, W.W., Rhodes, E., Measuring the efficiency of decision making units (1978) Eur. J. Oper. Res., 2 (6), pp. 429-444. , Nov; Sexton, T.R., Silkman, R.H., Hogan, A.J., Data envelopment analysis: Critique and extensions (1986) New Directions Program Eval., 1986 (32), pp. 73-105; Garg, D., Narahari, Y., Mechanism design for single leader stack-elberg problems and application to procurement auction design (2008) IEEE Trans. Autom. Sci. Eng., 5 (3), pp. 377-393. , Jul}, document_type={Article}, source={Scopus}, }`

- Carli, R. & Dotoli, M. (2020) A Dynamic Programming Approach for the Decentralized Control of Energy Retrofit in Large-Scale Street Lighting Systems. IN IEEE Transactions on Automation Science and Engineering, 17.1140-1157.

[Bibtex]`@ARTICLE{Carli20201140, author={Carli, R. and Dotoli, M.}, title={A Dynamic Programming Approach for the Decentralized Control of Energy Retrofit in Large-Scale Street Lighting Systems}, journal={IEEE Transactions on Automation Science and Engineering}, year={2020}, volume={17}, number={3}, pages={1140-1157}, doi={10.1109/TASE.2020.2966738}, art_number={9007032}, note={cited By 11}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087543588&doi=10.1109%2fTASE.2020.2966738&partnerID=40&md5=b2d2ba1ad644c81f451c0936fe963e18}, abstract={This article proposes a decision-making procedure that supports the city energy manager in determining the optimal energy retrofit plan of an existing public street lighting system throughout a wide urban area. The proposed decision model aims at simultaneously maximizing the energy consumption reduction and achieving an optimal allocation of the retrofit actions among the street lighting subsystems, while efficiently using the available budget. The resulting optimization problem is formulated as a quadratic knapsack problem. The proposed solution relies on a decentralized control algorithm that combines discrete dynamic programming with additive decomposition and value functions approximation. The optimality and complexity of the presented strategy are investigated, demonstrating that the proposed algorithm constitutes a fully polynomial approximation scheme. Simulation results related to a real street lighting system in the city of Bari (Italy) are presented to show the effectiveness of the approach in the optimal energy management of large-scale street lighting systems. Note to Practitioners-This article addresses the emerging need for decision support tools for the energy management of urban street lighting systems. The proposed decision-making strategy allows city energy managers and local policy makers taking retrofit decisions on an existing public street lighting system throughout a wide urban area. The presented strategy can be implemented in any engineering software, providing decision makers with a low-complexity and scalable Information and Communication Technology (ICT) tool for the optimization of the energy efficiency and environmental sustainability of street lighting systems. © 2004-2012 IEEE.}, author_keywords={Decision-making; dynamic programming; energy management; fully polynomial approximation scheme; optimization; urban street lighting}, keywords={Approximation algorithms; Budget control; Combinatorial optimization; Decentralized control; Decision making; Decision support systems; Energy efficiency; Energy management; Energy management systems; Energy utilization; Green computing; Lighting fixtures; Managers; Polynomial approximation; Retrofitting; Street lighting; Sustainable development, Additive decomposition; Decision making procedure; Decision support tools; Decision-making strategies; Environmental sustainability; Information and Communication Technologies; Quadratic knapsack problems; Street lighting system, Dynamic programming}, references={Richards, M., Carter, D., Good lighting with less energy: Where next? (2009) Lighting Res. Technol., 41 (3), p. 285; Sun, B., Luh, P.B., Jia, Q.-S., Jiang, Z., Wang, F., Song, C., Building energy management: Integrated control of active and passive heating, cooling, lighting, shading, and ventilation systems (2013) IEEE Trans. Autom. Sci. Eng., 10 (3), pp. 588-602. , Jul; (2013) Lighting the Cities-Accelerating the Deployment of Innovative Lighting in European Cities, , Luxembourg, U.k.: Office des Publications de L' Union Européenne European Commission; (2019) Streetlight-Epc Project Website, , http://www.streetlight-epc.eu, Accessed: Sep. [Online]; (2019) PearlLight+ Project Website, , http://hungarolux.hu/en/pearllight-project, Accessed: Sep. [Online]; (2019) Global Smart Street Lighting & Smart Cities: Market Forecast (2019-2028), , http://www.northeast-group.com, Northeast Group Apr. Accessed: Sep. 2019. [Online]; (2019) The Value of the Grid. Why Europe's Distribution Grids Matter in Decarbonising the Power System, , https://cdn.eurelectric.org/media/3921/value-of-the-grid-final-2019-030-0406-01-e-h-D1C80F0B.pdf, Eurelectric Jun., Accessed: Sep. 2019. [Online]; Polzin, F., Von Flotow, P., Nolden, C., Modes of governance for municipal energy efficiency services-The case of led street lighting in Germany (2016) J. Cleaner Prod., 139, pp. 133-145. , Dec; Campisi, D., Gitto, S., Morea, D., Economic feasibility of energy efficiency improvements in street lighting systems in Rome (2018) J. Cleaner Prod., 175, pp. 190-198. , Feb; Yoomak, S., Jettanasen, C., Ngaopitakkul, A., Bunjongjit, S., Leelajindakrairerk, M., Comparative study of lighting quality and power quality for led and hps luminaires in a roadway lighting system (2018) Energy Buildings, 159, pp. 542-557. , Jan; Pasolini, G., Toppan, P., Zabini, F., Castro, C.D., Andrisano, O., Design, deployment and evolution of heterogeneous smart public lighting systems (2019) Appl. Sci., 9 (16), p. 3281. , Aug; Lorente, D.G., Rabaza, O., Espín, A., García, A.P., Optimization of efficiency and energy saving in public lighting with multi-objective evolutionary algorithms (2017) Renew. Energies Power Qual. J., 1, pp. 62-65. , May; Rabaza, O., Peña-García, A., Pérez-Ocón, F., Gómez-Lorente, D., A simple method for designing efficient public lighting, based on new parameter relationships (2013) Expert Syst. Appl., 40 (18), pp. 7305-7315. , Dec; Cristea, M., Tîrnovan, R.A., Cristea, C., Pica, C.S., Fagarasan, C., A multi-criteria decision making approach for public lighting system selection (2018) Proc. MATEC Web Conf., 184; Lobão, J., Devezas, T., Catalão, J., Energy efficiency of lighting installations: Software application and experimental validation (2015) Energy Rep., 1, pp. 110-115. , Nov; Pizzuti, S., Annunziato, M., Moretti, F., Smart street lighting management (2013) Energy Efficiency, 6 (3), pp. 607-616. , Aug; Novak, T., Pollhammer, K., Zeilinger, H., Schaat, S., Intelligent streetlight management in a smart city (2014) Proc. IEEE Emerg. Technol. Factory Autom. (ETFA), pp. 1-8. , Sep; Urgiles, M.V., Arpi, P.E., Chacon-Troya, D.P., Lighting control actuator design and development for a ZigBee network with a Web server mounted on Raspberry Pi (2015) Proc. IEEE Int. Conf. Autom. Sci. Eng. (CASE), pp. 714-719. , Aug; Marino, F., Leccese, F., Pizzuti, S., Adaptive street lighting predictive control (2017) Energy Procedia, 111, pp. 790-799. , Mar; Pasc, P.-C., Dumitru, C.-D., Energy-efficient street lighting using a Mitsubishi alpha 2 plc based solution (2017) Procedia Eng., 181, pp. 824-828; Juntunen, E., Sarjanoja, E.-M., Eskeli, J., Pihlajaniemi, H., Österlund, T., Smart and dynamic route lighting control based on movement tracking (2018) Build. Environ., 142, pp. 472-483. , Sep; Burgos-Payan, M., Correa-Moreno, F.-J., Riquelme-Santos, J.-M., Improving the energy efficiency of street lighting. A case in the South of Spain (2012) Proc. 9th Int. Conf. Eur. Energy Market, pp. 1-8. , May; Radulovic, D., Skok, S., Kirincic, V., Energy efficiency public lighting management in the cities (2011) Energy, 36 (4), pp. 1908-1915. , Apr; Pintér, G., Baranyai, N., Wiliams, A., Zsiborács, H., Study of photovoltaics and LED energy efficiency: Case study in Hungary (2018) Energies, 11 (4), p. 790. , Mar; Beccali, M., Bonomolo, M., Ciulla, G., Galatioto, A., Lo Brano, V., Improvement of energy efficiency and quality of street lighting in South Italy as an action of sustainable energy action plans. The case study of Comiso (RG) (2015) Energy, 92, pp. 394-408. , Dec; Beccali, M., Energy saving and user satisfaction for a new advanced public lighting system (2019) Energy Convers. Manage., 195, pp. 943-957. , Sep; Tannous, S., Manneh, R., Harajli, H., El Zakhem, H., Comparative cradle-to-grave life cycle assessment of traditional grid-connected and solar stand-alone street light systems: A case study for rural areas in Lebanon (2018) J. Cleaner Prod., 186, pp. 963-977. , Jun; Beccali, M., Bonomolo, M., Leccese, F., Lista, D., Salvadori, G., On the impact of safety requirements, energy prices and investment costs in street lighting refurbishment design (2018) Energy, 165, pp. 739-759. , Dec; Carli, R., Dotoli, M., Cianci, E., An optimization tool for energy efficiency of street lighting systems in smart cities (2017) IFAC-PapersOnLine, 50 (1), pp. 14460-14464. , Jul; Carli, R., Dotoli, M., Pellegrino, R., A decision-making tool for energy efficiency optimization of street lighting (2018) Comput. Oper. Res., 96, pp. 223-235. , Aug; Carli, R., Dotoli, M., A decentralized control strategy for energy retrofit planning of large-scale street lighting systems using dynamic programming (2017) Proc. 13th IEEE Conf. Autom. Sci. Eng. (CASE), pp. 1196-1200. , Xi'an, China, Aug; Aronson, J.E., Liang, T.-P., Turban, E., (2005) Decision Support Systems and Intelligent Systems, , Upper Saddle River, NJ USA: Prentice-Hall; Lagorse, J., Paire, D., Miraoui, A., Sizing optimization of a standalone street lighting system powered by a hybrid system using fuel cell, PV and battery (2009) Renew. Energy, 34 (3), pp. 683-691. , Mar; Pisinger, D., The quadratic knapsack problem-a survey (2007) Discrete Appl. Math., 155 (5), pp. 623-648; Choi, J., Reveliotis, S., Relative value function approximation for the capacitated re-entrant line scheduling problem (2005) IEEE Trans. Autom. Sci. Eng., 2 (3), pp. 285-299. , Jul; Luh, P., Xiong, B., Chang, S.-C., Group elevator scheduling with advance information for normal and emergency modes (2008) IEEE Trans. Autom. Sci. Eng., 5 (2), pp. 245-258. , Apr; Chu, C., Chu, F., Zhou, M., Chen, H., Shen, Q., A polynomial dynamic programming algorithm for crude oil transportation planning (2012) IEEE Trans. Autom. Sci. Eng., 9 (1), pp. 42-55. , Jan; Vignali, R.M., Borghesan, F., Piroddi, L., Strelec, M., Prandini, M., Energy management of a building cooling system with thermal storage: An approximate dynamic programming solution (2017) IEEE Trans. Autom. Sci. Eng., 14 (2), pp. 619-633. , Apr; Bertsekas, D.P., (1995) Dynamic Programming and Optimal Control, 1-2. , Belmont, MA, USA: Athena Scientific; Sniedovich, M., (2010) Dynamic Programming: Foundations and Principles, , Boca Raton FL USA: CRC Press; Powell, W.B., (2007) Approximate Dynamic Programming: Solving the Curses of Dimensionality, 703. , Hoboken, NJ, USA: Wiley; Boyd, S., Vandenberghe, L., (2004) Convex Optimization, , Cambridge U.K.: Cambridge Univ. Press; Kellerer, H., Strusevich, V.A., Fully polynomial approximation schemes for a symmetric quadratic knapsack problem and its scheduling applications (2010) Algorithmica, 57 (4), pp. 769-795. , Aug; Carli, R., Dotoli, M., Pellegrino, R., A hierarchical decision-making strategy for the energy management of smart cities (2017) IEEE Trans. Autom. Sci. Eng., 14 (2), pp. 505-523. , Apr; Bruno, S., D'Aloia, M., De Benedictis, M., Lamonaca, S., Scala, M.L., Rotondo, G., Stecchi, U., (2011) Studio di Fattibilità per la Integrazione di un Modello di Pubblica Illuminazione Ad Alta Efficienza in un Power Park Urbano (Quartiere Eco-Sostenibile): Analisi di un Caso Pilota, , http://www.enea.it/it/Ricerca-sviluppo/documenti/ricerca-di-sistema-elettrico/smart-city/rds-328.pdf, (in Italian) [Online]; Rea, M.S., (2000) The IESNA Lighting Handbook, , New York, NY, USA: Illuminating Engineering Society of North America; (2018) Data Sheets of Road Lighting Products, , http://www.lighting.philips.com/main/systems/system-areas/roads-and-streets, Accessed: Oct., [Online]; (2018) List of Prices for Public Works in Apulia Region (in Italian), , http://www.regione.puglia.it/elenco-prezzi-2017, Accessed: Oct., [Online]; (2018) IBM ILOG CPLEX Optimization Studio Getting Started with CPLEX for MATLAB, , https://www.ibm.com/support/knowledgecenter/en/SSSA5P-12.6.2/ilog.odms.cplex.help/CPLEX/MATLAB/topics/gs.html, IBM Accessed: Oct. [Online]}, document_type={Article}, source={Scopus}, }`

- Cavone, G., Montaruli, V., Van Den Boom, T. J. J. & Dotoli, M. (2020) Demand-Oriented Rescheduling of Railway Traffic in Case of Delays IN 7th International Conference on Control, Decision and Information Technologies, CoDIT 2020., 1040-1045.

[Bibtex]`@CONFERENCE{Cavone20201040, author={Cavone, G. and Montaruli, V. and Van Den Boom, T.J.J. and Dotoli, M.}, title={Demand-Oriented Rescheduling of Railway Traffic in Case of Delays}, journal={7th International Conference on Control, Decision and Information Technologies, CoDIT 2020}, year={2020}, pages={1040-1045}, doi={10.1109/CoDIT49905.2020.9263874}, art_number={9263874}, note={cited By 0}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098278130&doi=10.1109%2fCoDIT49905.2020.9263874&partnerID=40&md5=77e95b60271d0546bb0ca7e53e9fbfc4}, abstract={The railway sector is currently experiencing a rapid evolution from fully manual towards automatic rail traffic control systems, due to the growth of transport demand and networks complexity. One of the main issues is to automatically and effectively reschedule the railway traffic in case of unexpected events, thus avoiding dramatic drops in the system performance. In the literature, the majority of contributions aims at automatically minimizing the train delays or optimizing the railway system performance (e.g., energy consumption). However, such strategies are not always able to ensure satisfaction of passengers that in many cases experience the side-effects of the rescheduling actions (e.g., cancellation of train runs, cancellation of coincidences, rerouting of trains, etc.). In this paper, we propose a demandoriented train rescheduling automatic technique that minimizes simultaneously the train delays and the discomfort perceived by passengers. When an unexpected event occurs, the rescheduling problem is set, based on the current state and nominal timetable of the system and its passengers flows. Hence, the problem is solved providing the control actions necessary to minimize both the delays and number of passengers subject to severe side-effects. The rescheduling is here formulated as a mixed integer linear programming problem, where the operating rules of the railway network are represented by linear equality and inequality constraints, while the objective is a linear function to be minimized. The possible control actions consist in re-timing the rail traffic and modifying the connections among lines. The proposed technique is preliminarily evaluated on a test case and a discussion is provided on the outcomes. © 2020 IEEE.}, author_keywords={demand-oriented rescheduling; passengers satisfaction; Railways}, keywords={Constraint theory; Energy utilization; Integer programming; Railroad traffic control; Railroads; Rails, Automatic rail traffic control system; Automatic technique; Inequality constraint; Linear functions; Mixed integer linear programming problems; Rescheduling problem; Train rescheduling; Unexpected events, Railroad transportation}, references={Yin, J., Tang, T., Yang, L., Xun, J., Huang, Y., Gao, Z., Research and development of automatic train operation for railway transportation systems: A survey (2017) Transportation Research Part C: Emerging Technologies, 85, pp. 548-572; Cao, Y., Ma, L., Zhang, Y., Application of fuzzy predictive control technology in automatic train operation (2019) Cluster Computing, 22 (6), pp. 14135-14144; Liang, Y., Liu, H., Qian, C., Wang, G., A modified genetic algorithm for multi-objective optimization on running curve of automatic train operation system using penalty function method (2019) International Journal of Intelligent Transportation Systems Research, 17 (1), pp. 74-87; Cacchiani, V., Huisman, D., Kidd, M., Kroon, L., Toth, P., Veelenturf, L., Wagenaar, J., An overview of recovery models and algorithms for real-time railway rescheduling (2014) Transportation Research Part B: Methodological, 63, pp. 15-37; Cavone, G., Dotoli, M., Epicoco, N., Seatzu, C., A decision making procedure for robust train rescheduling based on mixed integer linear programming and data envelopment analysis (2017) Applied Mathematical Modelling, 52, pp. 255-273; Ghaemi, N., Cats, O., Goverde, R., Railway disruption management challenges and possible solution directions (2017) Public Transport, 9 (1-2), pp. 343-364; Dotoli, M., Epicoco, N., Falagario, M., Turchiano, B., Cavone, G., Convertini, A., A decision support system for real-time rescheduling of railways (2014) 2014 European Control Conference (ECC, pp. 696-701. , ieee; Corman, F., D'Ariano, A., Marra, A., Pacciarelli, D., Samà, M., Integrating train scheduling and delay management in real-time railway traffic control (2017) Transportation Research Part E: Logistics and Transportation Review, 105, pp. 213-239; Josyula, S.P., Krasemann, J.T., Passenger-oriented railway traffic re-scheduling: A review of alternative strategies utilizing passenger flow data (2017) 7th International Conference on Railway Operations Modelling and Analysis, , Lille; Dollevoet, T., Huisman, D., Kroon, L., Schmidt, M., Schöbel, A., Delay management including capacities of stations (2015) Transportation Science, 49 (2), pp. 185-203; Kanai, S., Shiina, K., Harada, S., Tomii, N., An optimal delay management algorithm from passengers' viewpoints considering the whole railway network (2011) Journal of Rail Transport Planning & Management, 1 (1), pp. 25-37; Luan, X., Wang, Y., De Schutter, B., Meng, L., Lodewijks, G., Corman, F., Integration of real-time traffic management and train control for rail networks-part 1: Optimization problems and solution approaches (2018) Transportation Research Part B: Methodological, 115, pp. 41-71; Li, T., Sun, D., Jing, P., Yang, K., Smart card data mining of public transport destination: A literature review (2018) Information, 9 (1), p. 18; Østbø Sørensen, A., Bjelland, J., Bull-Berg, H., Landmark, A.D., Akhtar, M.M., Olsson, N., Use of mobile phone data for analysis of number of train travellers (2018) Journal of Rail Transport Planning & Management, 8 (2), pp. 123-144; Bemporad, A., Morari, M., Control of systems integrating logic, dynamics, and constraints (1999) Automatica, 35 (3), pp. 407-427; Cavone, G., Blenkers, L., Boom Den T.Van, Dotoli, M., Seatzu, C., De Schutter, B., Railway disruption: A bi-level rescheduling algorithm (2019) 2019 6th International Conference on Control, Decision and Information Technologies (CoDIT, pp. 54-59. , ieee; Kersbergen, B., Rudan, J., Boom Den T.Van, De Schutter, B., Towards railway traffic management using switching max-plus-linear systems (2016) Discrete Event Dynamic Systems, 26 (2), pp. 183-223}, document_type={Conference Paper}, source={Scopus}, }`

- Dotoli, M., Telmoudi, A. J., Toloo, M. & Viedma, E. H. (2020) Welcome IN 7th International Conference on Control, Decision and Information Technologies, CoDIT 2020..

[Bibtex]`@CONFERENCE{Dotoli2020, author={Dotoli, M. and Telmoudi, A.J. and Toloo, M. and Viedma, E.H.}, title={Welcome}, journal={7th International Conference on Control, Decision and Information Technologies, CoDIT 2020}, year={2020}, doi={10.1109/CoDIT49905.2020.9263895}, art_number={9263895}, note={cited By 0}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098237456&doi=10.1109%2fCoDIT49905.2020.9263895&partnerID=40&md5=630582e542b3b8e1e19d72249c100123}, document_type={Editorial}, source={Scopus}, }`

- Scarabaggio, P., Carli, R. & Dotoli, M. (2020) A fast and effective algorithm for influence maximization in large-scale independent cascade networks IN 7th International Conference on Control, Decision and Information Technologies, CoDIT 2020., 639-644.

[Bibtex]`@CONFERENCE{Scarabaggio2020639, author={Scarabaggio, P. and Carli, R. and Dotoli, M.}, title={A fast and effective algorithm for influence maximization in large-scale independent cascade networks}, journal={7th International Conference on Control, Decision and Information Technologies, CoDIT 2020}, year={2020}, pages={639-644}, doi={10.1109/CoDIT49905.2020.9263914}, art_number={9263914}, note={cited By 0}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098236199&doi=10.1109%2fCoDIT49905.2020.9263914&partnerID=40&md5=003f7d44f921ecd0bf436dacb2d3e136}, abstract={A characteristic of social networks is the ability to quickly spread information between a large group of people. The widespread use of online social networks (e.g., Facebook) increases the interest of researchers on how influence propagates through these networks. One of the most important research issues in this field is the so-called influence maximization problem, which essentially consists in selecting the most influential users (i.e., those who are able to maximize the spread of influence through the social network). Due to its practical importance in various applications (e.g., viral marketing), such a problem has been studied in several variants. Nevertheless, the current open challenge in the resolution of the influence maximization problem still concerns achieving a good trade-off between accuracy and computational time. In this context, based on independent cascade modeling of social networks, we propose a novel low-complexity and highly accurate algorithm for selecting an initial group of nodes to maximize the spread of influence in large-scale networks. In particular, the key idea consists in iteratively removing the overlap of influence spread induced by different seed nodes. The application to several numerical experiments based on real datasets proves that the proposed algorithm effectively finds practical near-optimal solutions of the addressed influence maximization problem in a computationally efficient fashion. Finally, the comparison with the state of the art algorithms demonstrates that in large scale scenarios the proposed approach shows higher performance in terms of influence spread and running time. © 2020 IEEE.}, keywords={Economic and social effects; Iterative methods, Computationally efficient; Influence maximizations; Most influential users; Near-optimal solutions; Numerical experiments; On-line social networks; Practical importance; State-of-the-art algorithms, Social networking (online)}, references={Wang, Y., Yu, C., Social interaction-based consumer decision-making model in social commerce: The role of word of mouth and observational learning (2017) International Journal of Information Management, 37 (3), pp. 179-189; Shakarian, P., Bhatnagar, A., Aleali, A., Shaabani, E., Guo, R., The independent cascade and linear threshold models (2015) Diffusion in Social Networks, pp. 35-48. , Springer; Lu, F., Zhang, W., Shao, L., Jiang, X., Xu, P., Jin, H., Scalable influence maximization under independent cascade model (2017) Journal of Network and Computer Applications, 86, pp. 15-23; Liben-Nowell, D., Kleinberg, J., Tracing information flow on a global scale using internet chain-letter data (2008) Proceedings of the National Academy of Sciences, 105 (12), pp. 4633-4638; Gomez-Rodriguez, M., Leskovec, J., Krause, A., Inferring networks of diffusion and influence (2012) Acm Transactions on Knowledge Discovery from Data (TKDD), 5 (4), p. 21; Domingos, P., Richardson, M., Mining the network value of customers (2001) Proceedings of the Seventh Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 57-66. , ACM; Richardson, M., Domingos, P., Mining knowledge-sharing sites for viral marketing (2002) Proceedings of the Eighth Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 61-70. , ACM; Kempe, D., Kleinberg, J., Tardos, E., Maximizing the spread of influence through a social network (2003) Proceedings of the Ninth Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 137-146. , ACM; Chen, W., Wang, C., Wang, Y., Scalable influence maximization for prevalent viral marketing in large-scale social networks (2010) Proceedings of the 16th Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 1029-1038; Kempe, D., Kleinberg, J., Tardos, E., Influential nodes in a diffusion model for social networks (2005) International Colloquium on Automata, Languages, and Programming, pp. 1127-1138. , Springer; Zhou, S., Cox, I.J., Hansen, L.K., Second-order assortative mixing in social networks (2017) International Workshop on Complex Networks, pp. 3-15. , Springer; Zhou, M.-Y., Xiong, W.-M., Wu, X.-Y., Zhang, Y.-X., Liao, H., Overlapping influence inspires the selection of multiple spreaders in complex networks (2018) Physica A: Statistical Mechanics and Its Applications, 508, pp. 76-83; Ma, L.-L., Ma, C., Zhang, H.-F., Wang, B.-H., Identifying influential spreaders in complex networks based on gravity formula (2016) Physica A: Statistical Mechanics and Its Applications, 451, pp. 205-212; Newman, M.E., Watts, D.J., Strogatz, S.H., Random graph models of social networks (2002) Proceedings of the National Academy of Sciences, 99, pp. 2566-2572; Rosa, D., Giua, A., On the spread of innovation in social networks (2013) Ifac Proceedings Volumes, 46 (27), pp. 322-327; Yang, W., Brenner, L., Giua, A., Influence maximization in independent cascade networks based on activation probability computation (2019) Ieee Access, 7, pp. 13745-13757; Yang, W., Brenner, L., Giua, A., Computation of activation probabilities in the independent cascade model (2018) 2018 5th International Conference on Control, Decision and Information Technologies, pp. 791-797. , IEEE; Aggarwal, C.C., Khan, A., Yan, X., On flow authority discovery in social networks (2011) Proceedings of the 2011 Siam International Conference on Data Mining. Siam, pp. 522-533; Yang, Y., Chen, E., Liu, Q., Xiang, B., Xu, T., Shad, S.A., On approximation of real-world influence spread (2012) Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 548-564. , Springer; Banerjee, S., Jenamani, M., Pratihar, D.K., (2018) A Survey on Influence Maximization in a Social Network, , arXiv preprint; Kunegis, J., Konect: The koblenz network collection (2013) Proceedings of the 22nd International Conference on World Wide Web, pp. 1343-1350}, document_type={Conference Paper}, source={Scopus}, }`

- Carli, R., Dotoli, M., Jantzen, J., Kristensen, M. & Ben Othman, S. (2020) Energy scheduling of a smart microgrid with shared photovoltaic panels and storage: The case of the Ballen marina in Samsø. IN Energy, 198..

[Bibtex]`@ARTICLE{Carli2020, author={Carli, R. and Dotoli, M. and Jantzen, J. and Kristensen, M. and Ben Othman, S.}, title={Energy scheduling of a smart microgrid with shared photovoltaic panels and storage: The case of the Ballen marina in Samsø}, journal={Energy}, year={2020}, volume={198}, doi={10.1016/j.energy.2020.117188}, art_number={117188}, note={cited By 33}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85082001819&doi=10.1016%2fj.energy.2020.117188&partnerID=40&md5=6477046a8e48af719b8ab95085e45579}, abstract={This paper focuses on the Model Predictive Control (MPC) based energy scheduling of a smart microgrid equipped with non-controllable (i.e., with fixed power profile) and controllable (i.e., with flexible and programmable operation) electrical appliances, as well as photovoltaic (PV) panels, and a battery energy storage system (BESS). The proposed control strategy aims at a simultaneous optimal planning of the controllable loads, the shared resources (i.e., the storage system charge/discharge and renewable energy usage), and the energy exchange with the grid. The control scheme relies on an iterative finite horizon on-line optimization, implementing a mixed integer linear programming energy scheduling algorithm to maximize the self-supply with solar energy and/or minimize the daily cost of energy bought from the grid under time-varying energy pricing. At each time step, the resulting optimization problem is solved providing the optimal operations of controllable loads, the optimal amount of energy to be bought/sold from/to the grid, and the optimal charging/discharging profile for the BESS. The proposed energy scheduling approach is applied to the demand side management control of the marina of Ballen, Samsø (Denmark), where a smart microgrid is currently being implemented as a demonstrator in the Horizon2020 European research project SMILE. Simulations considering the marina electric consumption (340 boat sockets, a service building equipped with a sauna and a wastewater pumping station, and the harbour master's office equipped with a heat pump), PV production (60kWp), and the BESS (237 kWh capacity) based on a public real dataset are carried out on a one year time series with a 1 h resolution. Simulations indicate that the proposed approach allows 90% exploitation of the production of the PV plant. Furthermore, results are compared to a naïve control approach. The MPC based energy scheduling improves the self-supply by 1.6% compared to the naïve control. Optimization of the business economy using the MPC approach, instead, yields to 8.2% savings in the yearly energy cost with respect to the naïve approach. © 2020 Elsevier Ltd}, author_keywords={Demand side management; Energy management; Energy storage; Microgrid; Model predictive control; On-line scheduling; Optimization algorithm; Renewable energy}, keywords={Costs; Demand side management; Economics; Electric utilities; Energy management; Energy storage; Energy utilization; Integer programming; Iterative methods; Marinas; Microgrids; Model predictive control; Photovoltaic cells; Predictive control systems; Pumping plants; Scheduling; Scheduling algorithms; Solar energy, Battery energy storage systems; European research project; Micro grid; Mixed integer linear programming; Online scheduling; Optimization algorithms; Renewable energies; Wastewater pumping station, Electric power system control, alternative energy; demand-side management; energy storage; exploitation; linear programing; optimization; photovoltaic system; pumping; smart grid; time series; wastewater, Denmark}, references={Piacentino, A., Duic, N., Markovska, N., Mathiesen, B.V., Guzović, Z., Eveloy, V., Lund, H., Sustainable and cost-efficient energy supply and utilisation through innovative concepts and technologies at regional, urban and single-user scales (2019) Energy, 182, pp. 254-268; Ranieri, L., Mossa, G., Pellegrino, R., Digiesi, S., Energy recovery from the organic fraction of municipal solid waste: a real options-based facility assessment (2018) Sustainability, 10 (2), p. 368; Kılkış, Ş., Composite index for benchmarking local energy systems of Mediterranean port cities (2015) Energy, 92, pp. 622-638; Fang, X., Misra, S., Xue, G., Yang, D., Smart grid — the new and improved power grid: a survey (2012) IEEE Communications Surveys Tutorials, 14 (4), pp. 944-980; Ma, R., Chen, H.-H., Huang, Y.-R., Meng, W., (2013), 4 (1), pp. 36-46. , “Smart grid communication: its challenges and opportunities,” IEEE Trans. Smart Grid, Mar; Talarico, C., D'Amato, G., Coviello, G., Avitabile, G., A high precision phase control unit for DDS-based PLLs for 2.4-GHz ISM band applications (2015) IEEE 58th International Midwest Symposium on Circuits and Systems (MWSCAS), pp. 1-4; Casalino, G., Castellano, G., Mencar, C., Incremental adaptive semi-supervised fuzzy clustering for data stream classification (2018) IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS), pp. 1-7; Lund, H., Werner, S., Wiltshire, R., Svendsen, S., Thorsen, J.E., Hvelplund, F., Mathiesen, B.V., 4th Generation District Heating (4GDH). Integrating smart thermal grids into future sustainable energy systems (2014) Energy, 68, pp. 1-11; Lund, Y.H., Østergaard, P.A., Chang, M., Werner, S., Svendsen, S., Sorknæs, P., Thorsen, J.E., Möller, B., The status of 4th generation district heating: research and results (2018) Energy, 164, pp. 147-159; Lund, H., Østergaard, P.A., Connolly, D., Mathiesen, B.V., Smart energy and smart energy systems (2017) Energy, 137, pp. 556-565; Palensky, P., Dietrich, D., Demand side management: demand response, intelligent energy systems, and smart loads (2011) IEEE Transactions on Industrial Informatics, 7 (3), p. 381¡388; Guelpa, E., Marincioni, L., Deputato, S., Capone, M., Amelio, S., Pochettino, E., Verda, V., Demand side management in district heating networks: a real application (2019) Energy, 182, pp. 433-442; Leitner, B., Widl, E., Gawlik, W., Hofmann, R., A method for technical assessment of power-to-heat use cases to couple local district heating and electrical distribution grids (2019) Energy, 182, pp. 729-738; Gelazanskas, L., Gamage, K.A., Demand side management in smart grid: a review and proposals for future direction (2014) Sustainable Cities and Society, 11, pp. 22-30; Kakran, S., Chanana, S., Smart operations of smart grids integrated with distributed generation: a review (2018) Renew Sustain Energy Rev, 81, pp. 524-535; Mathiesen, B.V., Lund, H., Connolly, D., Wenzel, H., Østergaard, P.A., Möller, B., Nielsen, S., Hvelplund, F.K., Smart Energy Systems for coherent 100% renewable energy and transport solutions (2015) Appl Energy, 145, pp. 139-154; Lund, H., Østergaard, P.A., Connolly, D., Ridjan, I., Mathiesen, B.V., Hvelplund, F., Thellufsen, J.Z., Sorknæs, P., Energy storage and smart energy systems (2016) International Journal of Sustainable Energy Planning and Management, 11; Fujimoto, Y., Distributed energy management for comprehensive utilization of residential photovoltaic outputs (2018) IEEE Trans. Smart Grid, 9 (2), pp. 1216-1227; Javaid, N., Hafeez, G., Iqbal, S., Alrajeh, N., Alabed, M.S., Guizani, M., Energy efficient integration of renewable energy sources in the smart grid for demand side management (2018) IEEE Access, 6, pp. 77077-77096; Giordano, A., Mastroianni, C., Menniti, D., Pinnarelli, A., Scarcello, L., Sorrentino, N., A two-stage approach for efficient power sharing within energy districts (2019) IEEE Transactions on Systems, Man, and Cybernetics: Systems; Digiesi, S., Mossa, G., Mummolo, G., Supply lead time uncertainty in a sustainable order quantity inventory model (2013) Manag Prod Eng Rev, 4 (4), pp. 15-27; Ma, J., Chen, H.H., Song, L., Li, Y., Residential load scheduling in smart grid: a cost efficiency perspective (2016) IEEE Trans. Smart Grid, 7, pp. 771-784; Yang, R., Wang, L., Multi-objective optimization for decision-making of energy and comfort management in building automation and control (2012) Sustainable Cities and Society, 2 (1), pp. 1-7; Arun, S.L., Selvan, M.P., Dynamic demand response in smart buildings using an intelligent residential load management system (2017) IET Gener, Transm Distrib, 11 (17), pp. 4348-4357; Lee, S., Lee, J., Jung, H., Cho, J., Hong, J., Lee, S., Har, D., Optimal power management for nanogrids based on technical information of electric appliances (2019) Energy Build, 191, pp. 174-186; Ye, F., Qian, Y., Hu, R.Q., A real-time information based demand-side management system in smart grid (2015) IEEE Trans Parallel Distr Syst, 27 (2), pp. 329-339; Liu, D., Xu, Y., Wei, Q., Liu, X., Residential energy scheduling for variable weather solar energy based on adaptive dynamic programming (2017) IEEE/CAA Journal of Automatica Sinica, 5 (1), pp. 36-46; Mahmud, K., Hossain, M.J., Town, G.E., Peak-load reduction by coordinated response of photovoltaics, battery storage, and electric vehicles (2018) IEEE Access, 6, pp. 29353-29365; Rahimiyan, M., Baringo, L., Strategic bidding for a virtual power plant in the day-ahead and real-time markets: a price-taker robust optimization approach (2015) IEEE Trans Power Syst, 31 (4), pp. 2676-2687; Fratean, A., Dobra, P., Control strategies for decreasing energy costs and increasing self-consumption in nearly zero-energy buildings (2018) Sustainable cities and society, 39, pp. 459-475; Serale, G., Fiorentini, M., Capozzoli, A., Bernardini, D., Bemporad, A., Model predictive control (MPC) for enhancing building and HVAC system energy efficiency: problem formulation, applications and opportunities (2018) Energies, 11 (3), p. 631; Yi, Z., Martin Böning, G., Mirra Santos, R., Challenges of implementing economic model predictive control strategy for buildings interacting with smart energy systems (2016) Journal of Applied Thermal Engineering, 114, pp. 1476-1486; Kolokotsa, D., Pouliezos, A., Stavrakakis, G., Lazos, C., Predictive control techniques for energy and indoor environmental quality management in buildings (2009) Build Environ, 44 (9), pp. 1850-1863; Li, S., Yang, J., Song, W., Chen, A., A real-time electricity scheduling for residential home energy management (2018) IEEE Internet of Things Journal, 6 (2), pp. 2602-2611; Hosseini, S.M., Carli, R., Dotoli, M., Model predictive control for real-time residential energy scheduling under uncertainties (2018) IEEE Int Conf Syst Man Cybern, pp. 1386-1391; Long, S., Marjanovic, O., Parisio, A., Generalised control-oriented modelling framework for multi-energy systems (2019) Appl Energy, 235, pp. 320-331; Nguyen, D.B., Scherpen, J.M., Bliek, F., Distributed optimal control of smart electricity grids with congestion management (2017) IEEE Trans Autom Sci Eng, 14 (2), pp. 494-504; Yang, X., Zhang, Y., He, H., Ren, S., Weng, G., Real-time demand side management for a microgrid considering uncertainties (2018) IEEE Transactions on Smart Grid, 10 (3), pp. 3401-3414; Prodan, I., Zio, E., A model predictive control framework for reliable microgrid energy management (2014) Int J Electr Power Energy Syst, 61, pp. 399-409; Du, Y., Wu, J., Li, S., Long, C., Onori, S., Coordinated energy dispatch of autonomous microgrids with distributed MPC optimization (2019) IEEE Transactions on Industrial Informatics; La Bella, A., Raimondi Cominesi, S., Sandroni, C., Scattolini, R., Hierarchical predictive control of microgrids in islanded operation (2017) IEEE Trans Autom Sci Eng, 14 (2), pp. 536-546; Oh, S., Chae, S., Neely, J., Baek, J., Cook, M., Efficient model predictive control strategies for resource management in an islanded Microgrid (2017) Energies, 10 (7), p. 1008; Pippia, T., Sijs, J., De Schutter, B., A parametrized model predictive control approach for microgrids. 2018 IEEE Conference on Decision and control (CDC) (2018), pp. 3171-3176; Zhang, Q., Grossmann, I.E., Enterprise-wide optimization for industrial demand side management: fundamentals, advances, and perspectives (2016) Chem Eng Res Des, 116, pp. 114-131; Matthews, B., Craig, I.K., Demand side management of a run-of-mine ore milling circuit (2013) Contr Eng Pract, 21 (6), pp. 759-768; Van Staden, A.J., Zhang, J., Xia, X., A model predictive control strategy for load shifting in a water pumping scheme with maximum demand charges (2011) Appl Energy, 88 (12), pp. 4785-4794; Jin, H., Li, Z., Sun, H., Guo, Q., Wang, B., Coordination on industrial load control and climate control in manufacturing industry under TOU prices (2017) IEEE Transactions on Smart Grid, 10 (1), pp. 139-152; Garcia, C.E., Prett, D.M., Morari, M., Model predictive control: theory and practice—a survey (1989) Automatica, 25 (3), pp. 335-348; Esther, B.P., Kumar, K.S., A survey on residential demand side management architecture, approaches, optimization models and methods (2016) Renew Sustain Energy Rev, 59, pp. 342-351; Ramanathan, B., Vittal, V., A framework for evaluation of advanced direct load control with minimum disruption (2008) IEEE Trans Power Syst, 23 (4), pp. 1681-1688; Carli, R., Dotoli, M., “Decentralized control for residential energy management of a smart users’ microgrid with renewable energy exchange (2019) IEEE/CAA J. Autom. Sin., 6 (3), pp. 641-656; Paul, S., Padhy, N.P., (2019), 15 (3), pp. 1566-1578. , “Resilient scheduling portfolio of residential devices and plug-in electric vehicle by minimizing conditional value at risk,” IEEE Trans. Industr. Inform., Mar; Jantzen, J., Requirements specification: deliverable D3.4 (2019), https://www.h2020smile.eu, [Internet] SMILE Available from: 2019, August; IBM ILOG CPLEX optimization studio getting started with CPLEX for MATLAB (2019), https://www.ibm.com/support/knowledgecenter/en/SSSA5P_12.6.2/ilog.odms.cplex.help/CPLEX/MATLAB/topics/gs.html, [August]; Østergaard, P.A., Jantzen, J., Marczinkowski, H.M., Kristensen, M., Business and socioeconomic assessment of introducing heat pumps with heat storage in small-scale district heating systems (2019) Renew Energy, 139, pp. 904-914; Jantzen, J., Kristensen, M., Christensen, T.H., Sociotechnical transition to smart energy: the case of Samso 1997-2030 (2018) Energy, 162, pp. 20-34; Marczinkowski, H.M., Østergaard, P.A., Evaluation of electricity storage versus thermal storage as part of two different energy planning approaches for the islands Samsø and Orkney (2019) Energy, 175, pp. 505-514; (2019), http://arkiv.energiinstituttet.dk/643/, [August]; (2019), https://www.nordpoolgroup.com/Market-data1/Dayahead/Area-Prices/ALL1/Hourly/?view=table, [August]; Kim, M., Parkt, S., Choi, J.K., Lee, J., Energy independence of energy trading system in microgrid. IEEE Innovative Smart Grid Technologies-Asia (ISGT-Asia) (2017), pp. 1-4}, document_type={Article}, source={Scopus}, }`

- Carli, R., Dotoli, M., Digiesi, S., Facchini, F. & Mossa, G. (2020) Sustainable scheduling of material handling activities in labor-intensive warehouses: A decision and control model. IN Sustainability (Switzerland), 12..

[Bibtex]`@ARTICLE{Carli2020, author={Carli, R. and Dotoli, M. and Digiesi, S. and Facchini, F. and Mossa, G.}, title={Sustainable scheduling of material handling activities in labor-intensive warehouses: A decision and control model}, journal={Sustainability (Switzerland)}, year={2020}, volume={12}, number={8}, doi={10.3390/SU12083111}, art_number={3111}, note={cited By 3}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084607073&doi=10.3390%2fSU12083111&partnerID=40&md5=225791095c6b1ea49a9be39370e2fff5}, abstract={In recent years, the continuous increase of greenhouse gas emissions has led many companies to investigate the activities that have the greatest impact on the environment. Recent studies estimate that around 10% of worldwide CO2 emissions derive from logistical supply chains. The considerable amount of energy required for heating, cooling, and lighting as well as material handling equipment (MHE) in warehouses represents about 20% of the overall logistical costs. The reduction of warehouses' energy consumption would thus lead to a significant benefit from an environmental point of view. In this context, sustainable strategies allowing the minimization of the cost of energy consumption due to MHE represent a new challenge in warehouse management. Consistent with this purpose, a two-step optimization model based on integer programming is developed in this paper to automatically identify an optimal schedule of the material handling activities of electric mobile MHEs (MMHEs) (i.e., forklifts) in labor-intensive warehouses from profit and sustainability perspectives. The resulting scheduling aims at minimizing the total cost, which is the sum of the penalty cost related to the makespan of the material handling activities and the total electricity cost of charging batteries. The approach ensures that jobs are executed in accordance with priority queuing and that the completion time of battery recharging is minimized. Realistic numerical experiments are conducted to evaluate the effects of integrating the scheduling of electric loads into the scheduling of material handling operations. The obtained results show the effectiveness of the model in identifying the optimal battery-charging schedule for a fleet of electric MMHEs from economic and environmental perspectives simultaneously. © 2020 by the authors.}, author_keywords={Battery charging; Decision and control; Demand-side management; Green warehouse; Material handling activity; Mobile material handling equipment; Optimization; Sustainable scheduling; Warehouse energy management}, keywords={carbon dioxide; carbon emission; cooling; cost analysis; decision making; environmental economics; environmental impact; equipment; fuel consumption; greenhouse gas; heating; optimization; strategic approach; supply chain management; sustainability}, references={Dhooma, J., Baker, P., An exploratory framework for energy conservation in existing warehouses (2012) Int. J. Logist. Res. Appl, 15, pp. 37-51; Doherty, S., Simchi-Levi, D., Roos, J., (2009) Supply Chain Decarbonization: The Role of Logistics and Transport in Reducing Supply Chain Carbon Emissions;, , Report Prepared with the Support of Accenture; World Economic Forum: Geneve, Switzerland; Bartolini, M., Bottani, E., Grosse, E.H., Bottani, E., Green warehousing: Systematic literature review and bibliometric analysis (2019) J. Clean. Prod, 226, pp. 242-258; D'Amato, G., Avitabile, G., Coviello, G., Talarico, C., Toward a novel architecture for beam steering of active phased-Array antennas (2016) Proceedings of the 2016 IEEE 59th International Midwest Symposium on Circuits and Systems (MWSCAS), pp. 1-4. , Abu Dhabi, UAE, 16-19 October; Bianchi, A., Pizzutilo, S., Vessio, G., Comparing AODV and N-AODV Routing Protocols for Mobile Ad-hoc Networks (2015) Proceedings of the 13th International Conference on Advances in Mobile Computing and Multimedia (MoMM 2015), pp. 159-168. , Brussels, Belgium, 11-13 December; Casalino, G., Castellano, G., Pasquadibisceglie, V., Zaza, G., Contact-Less Real-Time Monitoring of Cardiovascular Risk Using Video Imaging and Fuzzy Inference Rules (2018) Information, 10, p. 9; D'Amato, G., Avitabile, G., Coviello, G., Talarico, C., A beam steering unit for active phased-array antennas based on FPGA synthesized delay-lines and PLLs (2015) Proceedings of the 2015 International Conference on Synthesis, , In Modeling, Analysis and Simulation Methods and Applications to Circuit Design, SMACD 2015, Istanbul, Turkey, 7-9 September; Kozlowski, A., Searcy, C., Bardecki, M., Innovation for a Sustainable Fashion Industry: A Design Focused Approach toward the Development of New Business Models (2016) Green Fashion. Environmental Footprints and Eco-design ofProducts and Processes;, , In Springer: Singapore; Yang, B., Cho, H., Engine Technology and Research Trends of Advanced Biofuel As Alternative Fuel for Transportation Vehicles (2019) Int. J. Mech. Eng. Technol, 10, pp. 576-584; (2019) International Energy Outlook;, pp. 70-99. , Energy Information Administration: Washington, DC, USA; Zhang, Q., Grossmann, I.E., Enterprise-wide optimization for industrial demand side management: Fundamentals, advances, and perspectives (2016) Chem. Eng. Res. Des, 116, pp. 114-131; Song, M., Alvehag, K., Widén, J., Parisio, A., Estimating the impacts of demand response by simulating household behaviours under price and CO2 signals (2014) Electr. Power Syst. Res, 111, pp. 103-114; Strbac, G., Demand side management: Benefits and challenges (2008) Energy Policy, 36, pp. 4419-4426; Palensky, P., Dietrich, D., Demand Side Management: Demand Response, Intelligent Energy Systems, and Smart Loads (2011) IEEE Trans. Ind. Informatics, 7, pp. 381-388; Moon, J.-Y., Park, J., Smart production scheduling with time-dependent and machine-dependent electricity cost by considering distributed energy resources and energy storage (2013) Int. J. Prod. Res, 52, pp. 3922-3939; Fichtinger, J., Ries, J.M., Grosse, E.H., Baker, P., Assessing the environmental impact of integrated inventory and warehouse management (2015) Int. J. Prod. Econ, 270, pp. 717-729; Zulj, I., Glock, C.H., Grosse, E.H., Schneider, M., Picker routing and storage-assignment strategies for precedence-constrained order picking (2018) Comput. Ind. Eng, 223, pp. 338-347; Bortolini, M., Faccio, M., Ferrari, E., Gamberi, M., Pilati, F., Time and energy optimal unit-load assignment for automatic S/R warehouses (2017) Int. J. Prod. Econ, 290, pp. 133-145; Ghalehkhondabi, I., Masel, D.T., Storage allocation in a warehouse based on the forklifts fleet availability (2018) J. Algorithms Comput. Technol, 22, pp. 127-135; Meneghetti, A., Borgo, E.D., Monti, L., Rack shape and energy efficient operations in automated storage and retrieval systems (2015) Int. J. Prod. Res, 53, pp. 1-14; Meneghetti, A., Monti, L., Multiple-weight unit load storage assignment strategies for energy-efficient automated warehouses (2013) Int. J. Logist. Res. Appl, 27, pp. 304-322; Ene, S., Kügükoglu, I., Aksoy, A., Öztürk, N., A genetic algorithm for minimizing energy consumption in warehouses (2016) Energy, 224, pp. 973-980; Anand, V., Lee, S., Prabhu, V.V., Energy-Aware Models for Warehousing Operations (2014) IFIP Advances in Information and Communication Technology, 439. , In Springer: Berlin/Heidelberg, Germany; Taljanovic, K., Salihbegovic, A., A new strategies in picking from the forward pick locations (2009) Proceedings of the ICAT 2009-2009 22nd International Symposium on Information, , In Communication and Automation Technologies, Sarajevo, Bosnia and Herzegovina, 29-31 October; Boysen, N., Briskorn, D., Emde, S., Parts-to-picker based order processing in a rack-moving mobile robots environment (2017) Eur. J. Oper. Res, 262, pp. 550-562; Elbert, R.M., Franzke, T., Glock, C.H., Grosse, E.H., The effects of human behavior on the efficiency of routing policies in order picking: The case of route deviations (2017) Comput. Ind. Eng, 222, pp. 537-551; Dukic, G., Cesnik, V., Opetuk, T., Order-picking methods and technologies for greener warehousing (2010) Strojarstvo, 52, pp. 23-31; Fekete, P., Lim, S., Martin, S., Kuhn, K., Wright, N., Improved energy supply for non-road electric vehicles by occasional charging station location modelling (2016) Energy, 224, pp. 1033-1040; Akandere, G., The Effect of Logistic Businesses' Green Warehouse Management Practices on Business Performance (2016) Proceedings of the 25th International Academic Conference, pp. 10-23. , Paris, France, 6-9 September; Petljak, K., Zulauf, K., Stulec, I., Seuring, S., Wagner, R., Green supply chain management in food retailing: Survey-based evidence in Croatia (2018) Supply Chain Manag. Int. J, 23, pp. 1-15; Xin, L.J., Xien, K.C., Wahab, S.N., A Study on the Factors Influencing Green Warehouse Practice E3S Web of Conferences, Proceedings of the 2029 International Conference on Building Energy Conservation, Thermal Safety and Environmental Pollution Control (ICBTE 2029), , Hefei, China, 2-3 November 2029; In EDP Sciences: Les Ulis, France, 2019; Volume 136; Ries, J.M., Grosse, E.H., Fichtinger, J., Environmental impact of warehousing: A scenario analysis for the United States (2017) Int. J. Prod. Res, 55, pp. 6485-6499; Manghisi, V.M., Uva, A.E., Fiorentino, M., Gattullo, M., Boccaccio, A., Evangelista, A., Automatic Ergonomic Postural Risk Monitoring on the factory shopfloor-The ErgoSentinel Tool (2020) Procedia Manuf, 42, pp. 97-103; Manghisi, V.M., Uva, A.E., Fiorentino, M., Bevilacqua, V., Trotta, G.F., Monno, G., Real time RULA assessment using Kinect v2 sensor (2017) Appl. Ergon, 65, pp. 481-491; Pamungkas, D., Pipattanasomporn, M., Rahman, S., Hariyanto, N., Suwarno Impacts of Solar PV, Battery Storage and HVAC Set Point Adjustments on Energy Savings and Peak Demand Reduction Potentials in Buildings (2018) Proceedings of the 2018 International Conference and Utility Exhibition on Green Energy for Sustainable Development (ICUE), , In Phuket, Thailand, 24-26 October; Ardiyanto, D., Pipattanasomporn, M., Rahman, S., Hariyanto, N., Occupant-based HVAC Set Point Interventions for Energy Savings in Buildings (2018) Proceedings of the 2018 International Conference and Utility Exhibition on Green Energy for Sustainable Development (ICUE), , In Phuket, Thailand, 24-26 October; Minav, T., Laurila, L.I., Pyrhönen, J.J., Analysis of electro-hydraulic lifting system's energy efficiency with direct electric drive pump control (2013) Autom. Constr, 30, pp. 144-150; Minav, T., Virtanen, A., Laurila, L., Pyrhönen, J., Storage of energy recovered from an industrial forklift (2012) Autom. Constr, 22, pp. 506-515; Freis, J., Vohlidka, P., Günthner, W.A., Low-Carbon Warehousing: Examining Impacts of Building and Intra-Logistics Design Options on Energy Demand and the CO2 Emissions of Logistics Centers (2016) Sustainability, 8, p. 448; Schneider, M., Stenger, A., Goeke, D., The Electric Vehicle-Routing Problem with Time Windows and Recharging Stations (2014) Transp. Sci, 48, pp. 500-520; Schiffer, M., Schneider, M., Walther, G., Laporte, G., Vehicle Routing and Location Routing with Intermediate Stops: A Review (2019) Transp. Sci, 53, pp. 319-343; Carli, R., Dotoli, M., Decentralized control for residential energy management of a smart users microgrid with renewable energy exchange (2019) IEEE/CAA J. Autom. Sin, 6, pp. 641-656; Finn, P., Fitzpatrick, C., Demand side management of industrial electricity consumption: Promoting the use of renewable energy through real-time pricing (2014) Appl. Energy, 223, pp. 11-21; Xie, C., Allen, T.T., Simulation and experimental design methods for job shop scheduling with material handling: A survey (2015) Int. J. Adv. Manuf. Technol, 80, pp. 233-243; Minav, T., Hänninen, H., Sinkkonen, A., Laurila, L., Pyrhönen, J., Electric or hydraulic energy recovery systems in a reach truck-A comparison (2014) Stroj. Vestnik/J. Mech. Eng, 60, pp. 232-240; Glover, F., Improved Linear Integer Programming Formulations of Nonlinear Integer Problems (1975) Manag. Sci, 22, pp. 455-460; Gelebi, E., Fuller, J.D., Time-of-Use Pricing in Electricity Markets under Different Market Structures (2012) IEEE Trans. Power Syst, 27, pp. 1170-1181; Achterberg, T., SCIP: Solving constraint integer programs (2009) Math. Program. Comput, 2, pp. 1-41}, document_type={Article}, source={Scopus}, }`

- Dotoli, M., Epicoco, N. & Falagario, M. (2020) Multi-Criteria Decision Making techniques for the management of public procurement tenders: A case study. IN Applied Soft Computing Journal, 88..

[Bibtex]`@ARTICLE{Dotoli2020, author={Dotoli, M. and Epicoco, N. and Falagario, M.}, title={Multi-Criteria Decision Making techniques for the management of public procurement tenders: A case study}, journal={Applied Soft Computing Journal}, year={2020}, volume={88}, doi={10.1016/j.asoc.2020.106064}, art_number={106064}, note={cited By 19}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85077767281&doi=10.1016%2fj.asoc.2020.106064&partnerID=40&md5=eccbdda5b4995aaf45ca9d623d896b82}, abstract={Multi-Criteria Decision Making (MCDM) techniques are mathematical tools that help decision makers evaluating and ranking in an automatic way many possible alternatives over multiple conflicting criteria in highly complex situations. Several MCDM approaches exist, and their application fields are numerous, including the Supplier Selection Problem (SSP), which is an important problem in the management field. The aim of this paper is to perform a comparative analysis among some selected well-known MCDM techniques to show how they can properly support the specific decision making process of Public Procurement (PP) tenders, which is a particular type of the SSP, characterized by very stringent rules, thus requiring a specific assessment. Indeed, PP is a field characterized by the need for transparency, objectivity, and non-discrimination, which requires tendering organizations to explicitly state the adopted awarding method, the chosen decision criteria, and their relative importance in the call for proposals. However, this field has been seldomly investigated in the pertinent literature and thus the aim of this paper is to overcome such a limitation. In particular, this work focuses on the most commonly adopted methods in the field of supplier selection, namely the Analytic Hierarchy Process (AHP), the Preference Ranking Organization METHod for Enrichment of Evaluations (PROMETHEE), the Multi Attribute Utility Theory (MAUT), and the Data Envelopment Analysis (DEA). First, we adapt these techniques to the PP problem and its requirements. Then, by means of some real tenders at a European Institution, the selected techniques are compared with each other and with the currently adopted methodology in their classical deterministic setting, to identify which method best suits the specific requirements of PP tenders. Hence, since nowadays uncertainty is inherent in data from real applications, and can be modelled by expert evaluations through fuzzy logic, the comparison is extended to the fuzzy counterparts of two of the most promising selected approaches, i.e., the Fuzzy AHP and the Fuzzy DEA, showing that these methods can be effectively applied to the PP sector also in the presence of uncertainty on the tenders data. © 2020 Elsevier B.V.}, author_keywords={Compensatory models; Multi-Criteria Decision Making; Public procurement}, keywords={Analytic hierarchy process; Data envelopment analysis; Fuzzy logic; Hierarchical systems, Analytic hierarchy process (ahp); Comparative analysis; Decision making process; European institutions; Multi criteria decision making; Multi-criteria decision making technique; Multiattribute utility theory; Public procurement, Decision making}, references={Government at a Glance: Public procurement (2017), https://stats.oecd.org/Index.aspx?QueryId=78413, (accessed 4 December 2019); Thai, K.V., Piga, G., Advancing Public Procurement: Practices, Innovation and Knowledge Sharing (2007), PrAcademics Press Boca Raton Fla; Knight, L., Harland, C., Telgen, J., Thai, K.V., Callender, G., McKen, K., Public Procurement: International Cases and Commentary (2012), Routledge London, U. K; Directive 2014/24/EU (2019), https://eur-lex.europa.eu/legal-content/en/TXT/uri=CELEX:32014L0024, (accessed 4 December 2019); Directive 2014/25/EU (2019), https://eur-lex.europa.eu/eli/dir/2014/25/oj, (accessed 4 December 2019); Ishizaka, A., Nemery, P., Multi-Criteria Decision Analysis: Methods and Software (2013), Wiley; Velasquez, M., Hester, P.T., An analysis of Multi-Criteria Decision Making methods (2013) Int. J. Oper. Res., 10, pp. 56-66; Vommi, V.B., Kakollu, S.R., A simple approach to Multiple Attribute Decision Making using loss functions (2017) J. Ind. Eng. Int., 13, pp. 107-116; Chai, J., Ngai, E.W.T., Decision-making techniques in supplier selection: Recent accomplishments and what lies ahead (2020) Expert Syst. Appl., 140; Geldermann, J., Schöbel, A., On the similarities of some multi criteria decision analysis methods (2011) J. Multi-Criteria Decis. Anal., 18, pp. 219-230; Kahraman, C., Onar, S.C., Oztaysi, B., Fuzzy multicriteria decision-making: A literature review (2015) Int. J. Comput. Int. Sys., 8, pp. 637-666; Dotoli, M., Epicoco, N., Integrated network design of agile resource-efficient Supply Chains under uncertainty (2018) IEEE Trans. Syst. Man Cybern. Syst., pp. 1-15. , in press; Si, S.-L., You, X.-Y., Liu, H.-C., Zhang, P., DEMATEL technique: A systematic review of the state-of-the-art literature on methodologies and applications (2018) Math. Probl. Eng., 2018, pp. 1-33; León-Castro, E., Avilés-Ochoa, E., Merigó, J.M., Gil-Lafuente, A.M., Heavy moving averages and their application in econometric forecasting (2018) Cybern. Syst., 49, pp. 26-43; Khodadadzadeh, T., Sadjadi, S.J., A state-of-art review on Supplier Selection Problem (2013) Decis. Sci. Lett., 2, pp. 59-70; Chai, J., Liu, J.N.K., Ngai, E.W.T., Application of decision-making techniques in supplier selection: A systematic review of literature (2013) Expert Syst. Appl., 40, pp. 3872-3885; Önüt, S., Kara, S.S., Işik, E., Long term supplier selection using a combined fuzzy MCDM approach: A case study for a telecommunication company (2009) Expert Syst. Appl., 36, pp. 3887-3895; Dalalah, D., Hayajneh, M., Batieha, F., A fuzzy Multi-Criteria Decision Making model for supplier selection (2011) Expert Syst. Appl., 38, pp. 8384-8391; Dotoli, M., Falagario, M., A hierarchical model for optimal supplier selection in multiple sourcing contexts (2012) Int. J. Prod. Res., 50, pp. 2953-2967; Abdollahi, M., Arvan, M., Razmi, J., An integrated approach for supplier portfolio selection: Lean or agile? (2015) Expert Syst. Appl., 42, pp. 679-690; Parkan, C., Wu, M.L., Comparison of three modern multicriteria decision-making tools (2000) Internat. J. Systems Sci., 31, pp. 497-517; Wang, M., Lin, S.J., Lo, Y.C., The comparison between MAUT and PROMETHEE (2010) IEEE Int. Conf. Ind. Eng. Eng. Manag., pp. 753-757; Caterino, N., Iervolino, I., Manfredi, G., Cosenza, E., Comparative analysis of Multi-Criteria Decision-Making methods for seismic structural retrofitting (2009) Comput. Civ. Infrastruct. Eng., 24, pp. 432-445; Sen, B., Bhattacharjee, P., Mandal, U.K., A comparative study of some prominent Multi Criteria Decision Making methods for connecting rod material selection (2016) Perspect. Sci., 8, pp. 547-549; Mulliner, E., Malys, N., Maliene, V., Comparative analysis of MCDM methods for the assessment of sustainable housing affordability (2016) Omega, 59, pp. 146-156; Widianta, M.M.D., Rizaldi, T., Setyohadi, D.P.S., Riskiawan, H.Y., Comparison of multi-criteria decision support methods (AHP, TOPSIS, SAW & PROMETHEE) for employee placement (2018) J. Phys. Conf. Ser., 953, pp. 1-6; Van Weele, A., Purchasing and Supply Chain management: Analysis, strategy, planning and practice (2010) Cengage Learning EMEA, , http://books.google.com/books?hl=en&lr=&id=ZQr8T0tmH88C&pgis=1; Emrouznejad, A., Marra, M., The state of the art development of AHP (1979–2017): A literature review with a social network analysis (2017) Int. J. Prod. Res., 55, pp. 6653-6675; Bruno, G., Esposito, E., Genovese, A., Passaro, R., AHP-based approaches for supplier evaluation: Problems and perspectives (2012) J. Purch. Supply Manag., 18, pp. 159-172; Yadav, V., Sharma, M.K., Multi-criteria supplier selection model using the Analytic Hierarchy Process approach (2016) J. Model. Manag., 11, pp. 326-354; Dotoli, M., Epicoco, N., Falagario, M., A fuzzy technique for supply chain network design with quantity discounts (2017) Int. J. Prod. Res., 55, pp. 1862-1884; Ho, W., Xu, X., Dey, P.K., Multi-criteria decision making approaches for supplier evaluation and selection: A literature review (2010) European J. Oper. Res., 202, pp. 16-24; Data Envelopment Analysis: A technique for measuring the efficiency of government service delivery (1997) Agps, pp. 1-142; Segura, M., Maroto, C., A multiple criteria supplier segmentation using outranking and value function methods (2017) Expert Syst. Appl., 69, pp. 87-100; Shaik, M., Abdul-Kader, W., Green supplier selection generic framework: A Multi-Attribute Utility Theory approach (2011) Int. J. Sustain. Eng., 4, pp. 37-56; Saaty, T.L., Decision making with the analytic hierarchy process (2008) Int. J. Serv. Sci., 1, pp. 83-98; Ramanathan, R., A note on the use of the Analytic Hierarchy Process for environmental impact assessment (2001) J. Environ. Manag., 63, pp. 27-35; Macharis, C., Springael, J., De Brucker, K., Verbeke, A., PROMETHEE and AHP: The design of operational synergies in multicriteria analysis - Strengthening PROMETHEE with ideas of AHP (2004) European J. Oper. Res., 153, pp. 307-317; Millet, I., Wedley, W.C., Modelling risk and uncertainty with the analytic hierarchy process (2002) J. Multi-Criteria Decis. Anal., 11, pp. 97-107; Huang, S.H., Keskar, H., Comprehensive and configurable metrics for supplier selection (2007) Int. J. Prod. Econ., 105, pp. 510-523; Pérez, J., Jimeno, J.L., Mokotoff, E., Another potential shortcoming of AHP (2006) Top., 14, pp. 99-111; Triantaphyllou, E., Mann, S.H., Using the Analytic Hierarchy Process for decision making in engineering applications: Some challenges (1995) Int. J. Ind. Eng. Theory Appl. Pract., 2, pp. 35-44; Dyer, J.S., Multiattribute utility theory (MAUT) (2016) Int. Ser. Oper. Res. Manag. Sci., 233, pp. 285-314; Behzadian, M., Kazemzadeh, R.B., Albadvi, A., Aghdasi, M., PROMETHEE: A comprehensive literature review on methodologies and applications (2010) European J. Oper. Res., 200, pp. 198-215; Liesiö, J., Punkka, A., Baseline value specification and sensitivity analysis in multiattribute project portfolio selection (2014) European J. Oper. Res., 237, pp. 946-956; Charnes, A., Cooper, W.W., Rhodes, E., Measuring the efficiency of decision making units (1978) European J. Oper. Res., 2, pp. 429-444; Cooper, W.W., Seiford, L.M., Tone, K., Data Envelopment Analysis: A Comprehensive Text with Models, Applications, References and DEA-Solver Software (2007), Springer US; Dotoli, M., Epicoco, N., Falagario, M., Sciancalepore, F., A stochastic cross-efficiency Data Envelopment Analysis approach for supplier selection under uncertainty (2016) Int. Trans. Oper. Res., 23, pp. 725-748; Dotoli, M., Epicoco, N., Falagario, M., Sciancalepore, F., A cross-efficiency fuzzy Data Envelopment Analysis technique for performance evaluation of Decision Making Units under uncertainty (2015) Comput. Ind. Eng., 79, pp. 103-114; Sexton, T.R., Silkman, R.H., Hogan, A.J., Data Envelopment Analysis: Critique and extensions (1986) New Dir. Progr. Eval., 1986, pp. 73-105; Falagario, M., Sciancalepore, F., Costantino, N., Pietroforte, R., Using a DEA-cross efficiency approach in Public Procurement tenders (2012) European J. Oper. Res., 218, pp. 523-529; Doyle, J., Green, R., Efficiency and cross-efficiency in DEA derivations, meanings and uses (1994) J. Oper. Res. Soc., 45, pp. 567-578; Olesen, O.B., Petersen, N.C., Stochastic Data Envelopment Analysis—A review (2016) European J. Oper. Res., 251, pp. 2-21; Emrouznejad, A., Tavana, M., Hatami-Marbini, A., The state of the art in fuzzy Data Envelopment Analysis (2014) Perform. Meas. Fuzzy Data Envel. Anal. Stud. Fuzziness Soft Comput., 309, pp. 1-45; Zimmermann, H.-J., Fuzzy set theory review 2010 (2010) Wiley Interdiscip. Rev. Comput. Stat., 2, pp. 317-332; Wang, T.-C., Chen, Y.-H., Some issues on consistency of fuzzy Analytic Hierarchy Process (2006) 2006 Int. Conf. Mach. Learn. Cybern.; Kubler, S., Robert, J., Derigent, W., Voisin, A., Le Traon, Y., A state-of the-art survey & testbed of fuzzy AHP (FAHP) applications (2016) Expert Syst. Appl., 65, pp. 398-422; Kabir, G., Ahsan Akhtar Hasin, M., Comparative analysis of AHP and fuzzy AHP models for multicriteria inventory classification (2011) Int. J. Fuzzy Log. Syst., 1, pp. 1-16; Forman, E., Peniwati, K., Aggregating individual judgments and priorities with the Analytic Hierarchy Process (1998) European J. Oper. Res., 108, pp. 165-169; Skorupski, J., Multi-criteria group decision making under uncertainty with application to air traffic safety (2014) Expert Syst. Appl., 41, pp. 7406-7414}, document_type={Article}, source={Scopus}, }`

- Carli, R., Cavone, G., Othman, S. B. & Dotoli, M. (2020) IoT based architecture for model predictive control of HVAC systems in smart buildings. IN Sensors (Switzerland), 20..

[Bibtex]`@ARTICLE{Carli2020, author={Carli, R. and Cavone, G. and Othman, S.B. and Dotoli, M.}, title={IoT based architecture for model predictive control of HVAC systems in smart buildings}, journal={Sensors (Switzerland)}, year={2020}, volume={20}, number={3}, doi={10.3390/s20030781}, art_number={781}, note={cited By 27}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079071499&doi=10.3390%2fs20030781&partnerID=40&md5=b68287ad61a3091865fc7546425dce95}, abstract={The efficient management of Heating Ventilation and Air Conditioning (HVAC) systems in smart buildings is one of the main applications of the Internet of Things (IoT) paradigm. In this paper we propose an IoT based architecture for the implementation of Model Predictive Control (MPC) of HVAC systems in real environments. The considered MPC algorithm optimizes on line, in a closed-loop control fashion, both the indoor thermal comfort and the related energy consumption for a single zone environment. Thanks to the proposed IoT based architecture, the sensing, control, and actuating subsystems are all connected to the Internet, and a remote interface with the HVAC control system is guaranteed to end-users. In particular, sensors and actuators communicate with a remote database server and a control unit, which provides the control actions to be actuated in the HVAC system; users can set remotely the control mode and related set-points of the system; while comfort and environmental indices are transferred via the Internet and displayed on the end-users’ interface. The proposed IoT based control architecture is implemented and tested in a campus building at the Polytechnic of Bari (Italy) in a proof of concept perspective. The effectiveness of the proposed control algorithm is assessed in the real environment evaluating both the thermal comfort results and the energy savings with respect to a classical thermostat regulation approach. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.}, author_keywords={Heating ventilation and air conditioning system; Internet of things; Model predictive control; Predicted mean vote; Smart buildings}, keywords={Air conditioning; Closed loop control systems; Energy conservation; Energy utilization; Heat pump systems; HVAC; Intelligent buildings; Model predictive control; Predictive control systems; Thermal comfort, Closed-loop control; Control architecture; Efficient managements; Heating ventilation and air conditioning; Indoor thermal comfort; Internet of thing (IOT); Predicted mean vote; Sensors and actuators, Internet of things}, references={Ejaz, W., Naeem, M., Shahid, A., Anpalagan, A., Jo, M., Efficient energy management for the internet of things in smart cities (2017) IEEE Commun. Mag., 55, pp. 84-91; Digiesi, S., Mossa, G., Mummolo, G., Supply lead time uncertainty in a sustainable order quantity inventory model (2013) Manag. Prod. Eng. Rev., 4, pp. 15-27; Facchini, F., Mummolo, G., Mossa, G., Digiesi, S., Boenzi, F., Verriello, R., Minimizing the carbon footprint of material handling equipment: Comparison of electric and LPG forklifts (2016) J. Ind. Eng. Manag., 9, pp. 1035-1046; Dean, B., Dulac, J., Petrichenko, K., Graham, P., (2016) Towards Zero-Emission Efficient and Resilient Buildings: Global Status Report, , https://backend.orbit.dtu.dk/ws/portalfiles/portal/127199228/GABC_Global_Status_Report_V09_november.pdf; Carli, R., Dotoli, M., Pellegrino, R., Ranieri, L., A decision making technique to optimize a buildings’ stock energy efficiency (2016) IEEE Trans. Syst. Man Cybern. Syst., 47, pp. 794-807; Jouhara, H., Yang, J., Energy efficient HVAC systems (2018) Energy Build, 179, pp. 83-85; Afram, A., Janabi-Sharifi, F., Theory and applications of HVAC control systems—A review of model predictive control (MPC) (2014) Build. Environ., 72, pp. 343-355; Talarico, C., D’Amato, G., Coviello, G., Avitabile, G., A high precision phase control unit for DDS-based PLLs for 2.4-GHz ISM band applications (2015) Proceeding of the 2015 IEEE 58Th International Midwest Symposium on Circuits and Systems (MWSCAS), Fort Collins, pp. 1-4. , CO, USA, 2–5 May; Casalino, G., Castellano, G., Mencar, C., Data Stream Classification by Dynamic Incremental Semi-Supervised Fuzzy Clustering (2019) Int. J. Artif. Intell. Trans., 28; Serale, G., Fiorentini, M., Capozzoli, A., Bernardini, D., Bemporad, A., Model Predictive Control (MPC) for enhancing building and HVAC system energy efficiency: Problem formulation, applications and opportunities (2018) Energies, 11, p. 631; Serra, J., Pubill, D., Antonopoulos, A., Verikoukis, C., Smart, H.V.A.C., Control in IoT: Energy consumption minimization with user comfort constraints. Sci (2014) World J, 2014; Atzori, L., Iera, A., Morabito, G., The internet of things: A survey (2010) Comput. Netw., 54, pp. 2787-2805; Wu, F., Rüdiger, C., Yuce, M., Real-time performance of a self-powered environmental IoT sensor network system (2017) Sensors, 17, p. 282; Hazyuk, I., Ghiaus, C., Penhouet, D., Optimal temperature control of intermittently heated buildings using Model Predictive Control: Part II—Control algorithm (2012) Build. Environ., 51, pp. 388-394; Klaučo, M., Drgoňa, J., Kvasnica, M., Di Cairano, S., Building temperature control by simple mpc-like feedback laws learned from closed-loop data (2014) IFAC Proc, 47, pp. 581-586; Klaučo, M., Kvasnica, M., Explicit MPC approach to PMV-based thermal comfort control (2014) Proceeding of the 53Rd IEEE Conference on Decision and Control (CDC), pp. 4856-4861. , Los Angeles, CA, USA; (2005) ISO 7730: Ergonomics of the Thermal Environment—Analytical Determination and Interpretation of Thermal Comfort Using Calculation of the PMV and PPD Indices and Local Thermal Comfort Criteria, p. 60. , ISO: Geneva, Switzerland; Fanger, P.O., (1970) Thermal Comfort: Analysis and Application in Environment Engineering, p. 244. , Danish Technical Press: Copenhagen, Denmark; Shaikh, P.H., Nor, N.B.M., Nallagownden, P., Elamvazuthi, I., Ibrahim, T., A review on optimized control systems for building energy and comfort management of smart sustainable buildings (2014) Renew. Sustain. Energy Rev., 34, pp. 409-429; Xu, Z., Hu, G., Spanos, C.J., Schiavon, S., PMV-based event-triggered mechanism for building energy management under uncertainties (2017) Energy Build, 152, pp. 73-85; Alamin, Y.I., Del Mar Castilla, M., Álvarez, J.D., Ruano, A., An economic model-based predictive control to manage the users’ thermal comfort in a building (2017) Energies, 10, p. 321; Cigler, J., Prívara, S., Váňa, Z., Žáčeková, E., Ferkl, L., Optimization of predicted mean vote index within model predictive control framework: Computationally tractable solution (2012) Energy Build, 52, pp. 39-49; Corbin, C.D., Henze, G.P., May-Ostendorp, P., A model predictive control optimization environment for real-time commercial building application (2013) J. Build. Perform. Simu., 6, pp. 159-174; Ascione, F., Bianco, N., de Stasio, C., Mauro, G.M., Vanoli, G.P., Simulation-based model predictive control by the multi-objective optimization of building energy performance and thermal comfort (2016) Energy Build, 111, pp. 131-144; Carli, R., Cavone, G., Dotoli, M., Epicoco, N., Scarabaggio, P., Model predictive control for thermal comfort optimization in building energy management systems (2019) Proceeding of the 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC), pp. 2608-2613. , Bari, Italy, 6–9 October; Ramanathan, B., Vittal, V., A Framework for Evaluation of Advanced Direct Load Control With Minimum Disruption (2008) IEEE Trans. Power Syst., 23, pp. 1681-1688; ASHRAE Handbook: HVAC Systems and Equipment (1996) American Society of Heating, Refrigerating, and Air Conditioning Engineers, pp. 1-10. , Atlanta, GA, USA; Rode, C., Peuhkuri, R.H., Mortensen, L.H., Hansen, K.K., Time, B., Gustavsen, A., Ojanen, T., Arfvidsson, J., Moisture Buffering of Building Materials; BYG DTU-126 Report (2005) Department of Civil Engineering, Technical University of Denmark: Lyngby, Denmark, , https://backend.orbit.dtu.dk/ws/portalfiles/portal/2415500/byg-r126.pdf; Srinivas, J., (2004) Compr. Handbook of Mechanical Engineering, , Laxmi Publication: New Delhi, India; Murray, F.W., (1966) On the Computation of Saturation Vapor Pressure, , Technical Report; Rand Corp.: Santa Monica, CA, USA; Pippia, T., Sijs, J., de Schutter, B., A Parametrized Model Predictive Control Approach for Microgrids (2018) Proceeding of the IEEE Conference on Decision and Control, pp. 3171-3176. , Miami, FL, USA; García, C.E., Prett, D.M., Morari, M., Model predictive control: Theory and practice-A survey (1989) Automatica, 25, pp. 335-348; (2019) Beeta Home Page, , https://www.beeta.it/en/; Singh, M., Rajan, M.A., Shivraj, V.L., Balamuralidhar, P., Secure MQTT for Internet of Things (IoT (2015) Proceeding of the 2015 5Th International Conference on Communication Systems and Network Technologies (CSNT), pp. 746-751. , Gwalior, India}, document_type={Article}, source={Scopus}, }`

- Ben Cheikh-Graiet, S., Dotoli, M. & Hammadi, S. (2020) A Tabu Search based metaheuristic for dynamic carpooling optimization. IN Computers and Industrial Engineering, 140..

[Bibtex]`@ARTICLE{BenCheikh-Graiet2020, author={Ben Cheikh-Graiet, S. and Dotoli, M. and Hammadi, S.}, title={A Tabu Search based metaheuristic for dynamic carpooling optimization}, journal={Computers and Industrial Engineering}, year={2020}, volume={140}, doi={10.1016/j.cie.2019.106217}, art_number={106217}, note={cited By 6}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85077798452&doi=10.1016%2fj.cie.2019.106217&partnerID=40&md5=2652f6e49f0547e51b9a72ee41646328}, abstract={The carpooling problem consists in matching a set of riders’ requests with a set of drivers’ offers by synchro-nizing their origins, destinations and time windows. The paper presents the so-called Dynamic Carpooling Optimization System (DyCOS), a system which supports the automatic and optimal ridematching process be-tween users on very short notice or even en-route. Nowadays, there are numerous research contributions that revolve around the carpooling problem, notably in the dynamic context. However, the problem's high complex-ity and the real time aspect are still challenges to overcome when addressing dynamic carpooling. To counter these issues, DyCOS takes decisions using a novel Tabu Search based metaheuristic. The proposed algorithm employs an explicit memory system and several original searching strategies developed to make optimal deci-sions automatically. To increase users’ satisfaction, the proposed metaheuristic approach manages the transfer process and includes the possibility to drop off the passenger at a given walking distance from his destination or at a transfer node. In addition, the detour concept is used as an original aspiration process, to avoid the entrapment by local solutions and improve the generated solution. For a rigorous assessment of generated so-lutions, while considering the importance and interaction among the optimization criteria, the algorithm adopts the Choquet integral operator as an aggregation approach. To measure the effectiveness of the proposed method, we develop a simulation environment based on actual carpooling demand data from the metropolitan area of Lille in the north of France. © 2020 Elsevier Ltd}, author_keywords={Automatic ridematching; Choquet integral; Dynamic ridesharing; Multi-criterion optimization; Tabu search}, keywords={Dysprosium compounds; Integral equations; Tabu search, Automatic ridematching; Choquet integral; Meta-heuristic approach; Multi-criterion optimization; Optimization criteria; Optimization system; Ride-sharing; Simulation environment, Sulfur compounds}, references={Agatz, N., Erera, A., Savelsbergh, M.W., Wang, X., Dynamic ride-sharing: A simulation study in metro Atlanta (2011) Transportation Research Part B: Methodological, 45, pp. 1450-1464; Agatz, N., Erera, A., Savelsbergh, M., Wang, X., Optimization for dynamic ride-sharing: A review (2012) European Journal of Operational Research, 223 (2), pp. 295-303; Alonso-Mora, J., Samaranayake, S., Wallar, A., Frazzoli, E., Rus, D., On-demand high-capacity ride-sharing via dynamic trip-vehicle assignment (2017) Proceedings of the National Academy of Sciences, 114, pp. 462-467; Archetti, C., Speranza, M., Hertz, A., A tabu search algorithm for the split delivery vehicle routing problem (2006) Transporta-tion Science, 40, pp. 64-73; Berbeglia, G., Codeau, J., Laporte, G., A hybrid tabu search and constraint programming algorithm for dynamic dial a ride problem (2010) CIRRELT-2010-14; Blum, C., Roli, A., Metaheuristics in combinatorial optimization: Overview and conceptual comparison (2003) ACM comput-ing surveys (CSUR), 35, pp. 268-308; Bruglieria, M., Ciccarellib, D., Colornia, A., Poliunipool: A carpooling system for universities (2011) Procedia Social and Behavioral Sciences, 20, pp. 1450-1464; Burris, M., Winn, J.R., Slugging in houston: Casual carpool passenger characteristics (2006) Journal of Public Transportation, p. 2340; Calvo, R.W., de Luigi, F., Haastrup, P., Maniezzo, V., A distributed geographic information system for the daily car pooling problem (2004) Computers and Operations Research, 31, pp. 2263-2278; Carli, R., Dotoli, M., Epicoco, N., Angelico, B., Vinciullo, A., Automated evaluation of urban traffic congestion using bus as a probe (2015) Conference on automation science and engineering (IEEE CASE2015), Gothenburg, Sweden, pp. 967-972; http://www.ocmradio.com/uploads/fichetech/67article.pdf, [Car] Cartocom: Localisation de vehicules en temps reel avec l owasys 22a [online]. Available:; Cheikh, S., Hammadi, S., The alliance between optimization and multi-agent system for the management of the dynamic carpooling (2013) Agent and Multi-Agent Systems: Technologies and Applications, Advances in Intelligent Systems and Computing, 296, pp. 193-202; Cheikh, S., Hammadi, S., An optimized evolutionary multi-agent approach for regulation of disrupted urban transport (2013) International Journal of Modern Engineering Research (IJMER), 3, pp. 3841-3851; Cheikh, S.B., Hammadi, S., Multi-criterion tabu search to solve the dynamic carpooling based on the choquet integral aggregation (2014) Journal of Traffic and Logistics Engineering, 2; Cheikh, S.B., Hammadi, S., Multi-hop ridematching optimization problem: Intelligent chromosome agent-driven approach (2016) Expert Sytems with Applications, 62, pp. 161-176; Cheikh, S.B., Tahon, C., Hammadi, S., An evolutionary approach to solve the dynamic multi-hop ridematching problem (2017) Simulation: Transactions of the Society for Modeling and Simulation International, 93, pp. 3-19; Chen, C.-M., Shallcross, D., Shih, Y.-C., Wu, Y.-C., Kuo, S.-P., Hsu, Y.-Y., Holderby, Y., Smart ride share with flexible route matching (2011) 13th International Conference on Advanced Communication Technology (ICACT), pp. 1506-1510; Ching-Fang Liaw, C.C.W., Bander, J., A decision support system for the bimodal dial-a-ride problem (1996) IEEE Transac-tions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 26, pp. 552-565; Clemente, M., Fanti, M.P., Iacobellis, G., Nolich, M., Ukovich, W., A decision support system for user-based vehicle relocation in car sharing systems (2018) IEEE Transactions on Systems, Man, and Cybernetics: Systems, 48, pp. 1283-1296; Cordeau, J., Laporte, G., A tabu search heuristic for the static multi-vehicle dial-a-ride problem (2003) Transportation Research, part B, p. 579594; Cordeau, J.F., Laporte, G., Tabu search heuristics for the vehicle routing problem (2005) Metaheuristic Optimization via Memory and Evolution Operations Research/Computer Science Interfaces Series, 30, pp. 145-163; Czioska, P., Mattfeld, D.C., Sester, M., Gis-based identification and assessment of suitable meeting point locations for ride-sharing (2017) Transportation Research Procedia, 22, pp. 314-324; Dimitrakopoulos, G., Demestichas, P., Koutra, V., Intelligent management functionality for improving transportation efficiency by means of the car pooling concept (2012) IEEE Transactions on Intelligent Transportation Systems, 13, pp. 424-436; Dotoli, M., Fanti, M., Meloni, C., A signal timing plan formulation for urban traffic control (2006) Control Engineering Practice, 14, pp. 1297-1311; Dotoli, M., Hammadi, S., Jeribi, K., Russo, C., Zgaya, H., A multi-agent decision support system for optimization of co-modal transportation route planning services (2013) 52nd Conference on Decision and Contro (IEEE CDC), Florence, Italy, pp. 911-916; Fanti, M.P., Mangini, A.M., Pedroncelli, G., Ukovich, W., Fleet sizing for electric car sharing systems in discrete event system frameworks (2017) IEEE Transactions on Systems, Man, and Cybernetics: Systems; Ferreira, J., Trigo, P., Filipe, P., Collaborative car pooling system (2009) World Academy of Science, Engineering and Technology, 54, p. 2846; Fiechter, C., A parallel tabu search algorithm for large traveling salesman problems (1994) Discrete Applied Mathematics, 51, pp. 243-267; Furuhata, M., Dessouky, M., Ordez, F., Brunet, M., Wang, X., Koenig, S., Ridesharing: The state-of-the-art and future directions (2013) Transportation Research Part B: Methodological, 57, p. 2846; Geisberger, R., Luxen, D., Neubauer, S., Sanders, P., Volker, L., Fast detour computation for ride sharing (2009) Clinical Orthopaedics and Related Research, , abs/0907.5269; Gendreau, M., Potvin, J., Tabu search (2005) Search methodologies, pp. 165-186. , Springer; Grabisch, M., Roubens, M., Applications of the choquet integral in multicriteria decision making. Fuzzy measures and integrals: theories and applications (2002) Phisica Verlag, pp. 415-434; Graziotin, D., An analysis of issues against the adoption of dynamic carpooling (2010), pp. 1-6. , Free University of Bozen-Bolzano; Gruebele, P., Interactive system for real time dynamic multi-hop carpooling (2008) Global Transport, , Knowledge Partnership; Hayward, G., Davidson, V., (2003), Fuzzy logic applications. In Analyst; Herbawi, W., Weber, M., Evolutionary multiobjective route planning in dynamic multi-hop ridesharing (2011) Evolutionary Computation in Combinatorial Optimization, 6622, pp. 84-95; Herbawi, W., Weber, M., Modeling the multihop ridematching problem with time windows and solving it using genetic algorithms (2012) IEEE 24th international conference on tools with artificial intelligence, pp. 89-96; (2009), http://nhts.ornl.gov, Hts: National household travel survey. u.s. department of transportation, federal highway administration [online]. Available:; Huang, S., Jiau, M., Lin, C., A genetic-algorithm-based approach to solve carpool service problems in cloud computing (2015) IEEE Transactions on Intelligent Transportation Systems, 16, pp. 866-876; Huang, C., Zhang, D., Si, Y.W., Leung, S.C., Tabu search for the real-world carpooling problem (2016) Journal of Combinatorial Optimization, 32, pp. 492-512; Jespersen-Groth, J., Potthoff, D., Clausen, J., Disruption management in passenger railway transportation (2009) Robust and online large-scale optimization, pp. 399-421. , Springer Berlin, Heidelberg; Khalilpourazari, S., Khalilpourazary, S., Optimization of time, cost and surface roughness in grinding process using a robust multi-objective dragonfly algorithm (2018) Neural Computing and Applications, pp. 1-12; Khalilpourazari, S., Khalilpourazary, S., Scwoa: an efficient hybrid algorithm for parameter optimization of multi-pass milling process (2018) Journal of Industrial and Production Engineering, 35, pp. 35-147; Khalilpourazari, S., Pasandideh, S.H.R., Multi-objective optimization of multi-item eoq model with partial backordering and defective batches and stochastic constraints using mowca and mogwo (2018) Operational Research, pp. 1-33; Khalilpourazari, S., Pasandideh, S.H.R., Modeling and optimization of multi-item multi-constrained eoq model for growing items (2019) Knowledge-Based System, 164, pp. 150-162; Khalilpourazari, S., Pasandideh, S.H.R., Ghodratnama, A., Robust possibilistic programming for multi-item eoq model with defective supply batches: Whale optimization and water cycle algorithms (2018) Neural Computing and Applications, pp. 1-28; Kleiner, A., Nebel, B., Ziparo, V., A mechanism for dynamic ride sharing based on parallel auctions (2011) Proc of the 22th international joint conference on artificial intelligence (IJCAI), Barcelona, Spain, p. 266272; Kris, B., Katrien, R., Inneke, V.N., The vehicle routing problem: State of the art classification and review (2016) Computers Industrial Engineering, 99, pp. 300-313; Lalos, P., Korres, A., Datsikas, C., Tombras, G., Peppas, K., A framework for dynamic car and taxi pools with the use of positioning systems (2009) 2009 computation world: Future computing, service computation, cognitive, adaptive, content, patterns, pp. 385-391; Lecchini, A., Glover, W., Lygeros, J., Maciejowski, J., Monte carlo optimisation for conflict resolution in air traffic control (2006) IEEE Transactions on Intelligent Transportation Systems, pp. 470-482; Likhachevt, M., Ferguson, D., Gordon, G., Stentz, A., Thrun, S., (2005), Anytime dynamic a*: An anytime, replanning algorithm; Mariagrazia, D., Nicola, E., Marco, F., A cross-efficiency fuzzy data envelopment analysis technique for performance evaluation of decision making units under uncertainty (2015) Computers and Industrial Engineering, 79, pp. 103-114; Mudaliar, D.N., Modi, N.K., Unraveling travelling salesman problem by genetic algorithm using m-crossover operator (2013) Signal Processing Image Processing and Pattern Recognition (ICSIPR), pp. 127-130; Murofushi, T., Sugeno, M., A theory of fuzzy measures: Representations, the choquet integral, and null sets (1991) Journal of Mathematical Analysis and Applications, pp. 532-549; Nagare, D., More, K., Tanwar, N., Kulkarni, S.S., Gunda, K.C., Dynamic carpooling application development on android platform (2013) International Journal of Innovative Technology and Exploring Engineering (IJITEE), 2, pp. 136-139; Nanry, W., Barnes, W., Solving the pickup and delivery problem with time windows using reactive tabu (2000) Transportation Research Part B: Methodological, p. 107121; Osman, I.H., Laporte, G., Metaheuristics: A bibliography (1996) Annals of Operations Research, 63, pp. 513-623; Schrank, D., Eisele, B., Lomax, T., (2012), http://d2dtl5nnlpfr0r.cloudfront.net/tti.tamu.edu/documents/mobility-report-2012.pdf, Ttis 2012 urban mobility report powered by inrix traffic data. In Texas AM Transportation Institute [online]. Available:; Seyedabrishami, S., Mamdoohi, A., Barzegar, A., Hasanpour, S., Impact of carpooling on fuel saving in urban transportation: Case study of Tehran (2012) Procedia - Social and Behavioral Sciences, 54, p. 323331; Sghaier, M., Hammadi, S., Zgaya, H., Tahon, C., An optimized dynamic carpooling system based on communicating agents operating over a distributed architecture (2011) 11th international conference on intelligent systems design and applications (ISDA), pp. 124-129; Sghaier, M., Hammadi, S., Zgaya, H., Tahon, C., A novel approach based on a distributed dynamic graph modeling set up over a subdivision process to deal with distributed optimized real time carpooling requests (2011) 14th international IEEE conference on in intelligent transportation systems (ITSC), pp. 1311-1316; Shete, A., Bhandare, V., Londhe, L., Mali, P., Intelligent carpooling system (2015) International Journal of Computer Applications, 118, pp. 26-31; Shi, L., Pan, Y., An efficient search method for job-shop scheduling problems (2005) IEEE Transactions on Automation Science and Engineering, pp. 73-77; (2011), http://www.scotland.gov.uk/topics/statistics/browse/transport-travel/trendcaroccupancy, Shs: Private transport - car occupancy [online]. Available:; Sidi, M.O., Contribution a l'amelioration des systemes d'aide a la decision dans le domaine du transport (2006), These de doctorat Ecole Centrale de Lille; Sidi, M.O., Hammadi, S., Urban transport networks regulation and evaluation: A fuzzy evolutionary approach (2008) IEEE Transport System, Man, Cybern SMC Part A, p. 309318; Sidi, M.M.O., Hayat, S., Hammadi, S., Borne, P., A novel approach to developing and evaluating regulation strategies for urban transport disrupted networks (2008) International Journal of Computer Integrated Manufacturing, pp. 480-493; Son, T., Hoai, L., Taol, P., Khadraoui, D., A distributed algorithm solving multiobjective dynamic car pooling problem (2012) International Conference on Computer and Information Science (ICCIS), pp. 231-236; Wang, X., Dessouky, M., Ordonez, F., A pickup and delivery problem for ridesharing considering congestion (2015), Maney Publishing; (2004), http://www.cities.worldcarshare.com, Wcc: The world carshare consortium; (2011), https://fr.wikipedia.org/wiki/analyse, Wikipedia [online]. Available: de la complexite des algorithmes; Xia, J., Curtin, K.M., Li, W., Zhao, Y., A new model for a carpool matching service (2015) PloS ones, 10, pp. 462-467; Xiao, Q., He, R.C., Carpooling scheme selection for taxi carpooling passengers: a multi-objective model and optimi-sation algorithm (2017) Archives of Transport, 42; Xingquan, Z., Murray, C., Smith, A., Solving an extended double row layout problem using multiobjective tabu search and linear programming (2014) IEEE Transactions on in Automation Science and Engineering, 11, pp. 1122-2113}, document_type={Article}, source={Scopus}, }`

- Scarabaggio, P., Grammatico, S., Carli, R. & Dotoli, M. (2020) A distributed, rolling-horizon demand side management algorithm under wind power uncertainty IN IFAC-PapersOnLine., 12620-12625.

[Bibtex]`@CONFERENCE{Scarabaggio202012620, author={Scarabaggio, P. and Grammatico, S. and Carli, R. and Dotoli, M.}, title={A distributed, rolling-horizon demand side management algorithm under wind power uncertainty}, journal={IFAC-PapersOnLine}, year={2020}, volume={53}, number={2}, pages={12620-12625}, doi={10.1016/j.ifacol.2020.12.1830}, note={cited By 0}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105077171&doi=10.1016%2fj.ifacol.2020.12.1830&partnerID=40&md5=ee3d9dd2c0187078aed713ccc87a026b}, abstract={In this paper, we consider a smart grid where users behave selfishly, aiming at minimizing cost in the presence of uncertain wind power availability. We adopt a demand side management (DSM) model, where active users (so-called prosumers) have both private generation and local storage availability. These prosumers participate to the DSM strategy by updating their energy schedule, seeking to minimize their local cost, given their local preferences and the global grid constraints. The energy price is defined as a function of the aggregate load and the wind power availability. We model the resulting problem as a non-cooperative Nash game and propose a semi-decentralized algorithm to compute an equilibrium. To cope with the uncertainty in the wind power, we adopt a rolling-horizon approach, and in addition we use a stochastic optimization technique. We generate several wind power production scenarios from a defined probability density function (PDF), determining an approximate stochastic cost function. Simulations results on a real dataset show that the proposed approach generates lower individual costs compared to a standard expected value approach. Copyright © 2020 The Authors. This is an open access article under the CC BY-NC-ND license}, author_keywords={Demand side management; Sample average approximation; Smart grid; Stochastic optimization}, references={Afshar, K., Shokri Gazafroudi, A., Application of stochastic programming to determine operating reserves with considering wind and load uncertainties (2007) Journal of Operation and Automation in Power Engineering, 1 (2), pp. 96-109; Aghajani, G., Shayanfar, H., Shayeghi, H., Demand side management in a smart micro-grid in the presence of renewable generation and demand response (2017) Energy, 126, pp. 622-637; Atzeni, I., Ordóñez, L.G., Scutari, G., Palomar, D.P., Fonollosa, J.R., Demand-side management via distributed energy generation and storage optimization (2012) IEEE Transactions on Smart Grid, 4 (2), pp. 866-876; Belgioioso, G., Grammatico, S., Projected-gradient algorithms for generalized equilibrium seeking in aggregative games are preconditioned forward-backward methods (2018) 2018 Eur. Control Conf. ECC 2018, pp. 2188-2193; Biswas, P.P., Suganthan, P., Amaratunga, G.A., Optimal power flow solutions incorporating stochastic wind and solar power (2017) Energy Conversion and Management, 148, pp. 1194-1207; Carli, R., Dotoli, M., Decentralized control for residential energy management of a smart users' microgrid with renewable energy exchange (2019) IEEE/CAA Journal of Automatica Sinica, 6 (3), pp. 641-656; Estrella, R., Belgioioso, G., Grammatico, S., A shrinking-horizon, game-theoretic algorithm for distributed energy generation and storage in the smart grid with wind forecasting (2019) IFAC-PapersOnLine, 52 (3), pp. 126-131; Gao, B., Zhang, W., Tang, Y., Hu, M., Zhu, M., Zhan, H., Game-theoretic energy management for residential users with dischargeable plug-in electric vehicles (2014) Energies, 7 (11), pp. 7499-7518; Iizaka, T., Jintsugawa, T., Kondo, H., Nakanishi, Y., Fukuyama, Y., Mori, H., A wind power forecasting method and its confidence interval estimation (2014) Electrical Engineering in Japan, 186 (2), pp. 52-60; Kim, S., Pasupathy, R., Henderson, S.G., A guide to sample average approximation (2015) Handbook of Simulation Optimization, pp. 207-243. , Springer; Ko, W., Hur, D., Park, J.K., Correction of wind power forecasting by considering wind speed forecast error (2015) Journal of International Council on Electrical Engineering, 5 (1), pp. 47-50; Kun, Y., Zhang, K., Zheng, Y., Dawei, L., Ying, W., Zhenglin, Y., Irregular distribution of wind power prediction (2018) Journal of Modern Power Systems and Clean Energy, 6 (6), pp. 1172-1180; Pazouki, S., Haghifam, M.R., Moser, A., Uncertainty modeling in optimal operation of energy hub in presence of wind, storage and demand response (2014) International Journal of Electrical Power & Energy Systems, 61, pp. 335-345; Schinazi, R.B., Transformations of random variables and random vectors (2012) Probability with Statistical Applications, pp. 201-268. , Springer; Shapiro, A., (2008) Stochastic Programming Approach to Optimization under Uncertainty, 112. , Springer US}, document_type={Conference Paper}, source={Scopus}, }`

- Cavone, G., van den Boom, T., Blenkers, L., Dotoli, M., Seatzu, C. & De Schutter, B. (2020) An MPC-Based Rescheduling Algorithm for Disruptions and Disturbances in Large-Scale Railway Networks. IN IEEE Transactions on Automation Science and Engineering, ..

[Bibtex]`@ARTICLE{Cavone2020, author={Cavone, G. and van den Boom, T. and Blenkers, L. and Dotoli, M. and Seatzu, C. and De Schutter, B.}, title={An MPC-Based Rescheduling Algorithm for Disruptions and Disturbances in Large-Scale Railway Networks}, journal={IEEE Transactions on Automation Science and Engineering}, year={2020}, doi={10.1109/TASE.2020.3040940}, note={cited By 1}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098771253&doi=10.1109%2fTASE.2020.3040940&partnerID=40&md5=155dc1581a4c1e8c1c487e2eb7c75cbd}, abstract={Railways are a well-recognized sustainable transportation mode that helps to satisfy the continuously growing mobility demand. However, the management of railway traffic in large-scale networks is a challenging task, especially when both a major disruption and various disturbances occur simultaneously. We propose an automatic rescheduling algorithm for real-time control of railway traffic that aims at minimizing the delays induced by the disruption and disturbances, as well as the resulting cancellations of train runs and turn-backs (or short-turns) and shuntings of trains in stations. The real-time control is based on the Model Predictive Control (MPC) scheme where the rescheduling problem is solved by mixed integer linear programming using macroscopic and mesoscopic models. The proposed resolution algorithm combines a distributed optimization method and bi-level heuristics to provide feasible control actions for the whole network in short computation time, without neglecting physical limitations nor operations at disrupted stations. A realistic simulation test is performed on the complete Dutch railway network. The results highlight the effectiveness of the method in properly minimizing the delays and rapidly providing feasible feedback control actions for the whole network. IEEE}, author_keywords={Delays; Feedback control; Heuristic algorithms; Mixed Integer Linear (MIL) Programming (MILP); Model Predictive Control (MPC); Optimization; Prediction algorithms; Rail transportation; railway traffic disruption; Real-time systems; rescheduling algorithms.}, keywords={Heuristic methods; Integer programming; Model predictive control; Predictive control systems; Railroads; Real time control, Distributed optimization; Large-scale network; Mixed integer linear programming; Physical limitations; Realistic simulation; Rescheduling problem; Resolution algorithms; Sustainable transportation, Automatic train control}, document_type={Article}, source={Scopus}, }`

- Carli, R., Cavone, G., Epicoco, N., Scarabaggio, P. & Dotoli, M. (2020) Model predictive control to mitigate the COVID-19 outbreak in a multi-region scenario. IN Annual Reviews in Control, 50.373-393.

[Bibtex]`@ARTICLE{Carli2020373, author={Carli, R. and Cavone, G. and Epicoco, N. and Scarabaggio, P. and Dotoli, M.}, title={Model predictive control to mitigate the COVID-19 outbreak in a multi-region scenario}, journal={Annual Reviews in Control}, year={2020}, volume={50}, pages={373-393}, doi={10.1016/j.arcontrol.2020.09.005}, note={cited By 19}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097753585&doi=10.1016%2fj.arcontrol.2020.09.005&partnerID=40&md5=adce49e71a999948867e93de3ae2e142}, abstract={The COVID-19 outbreak is deeply influencing the global social and economic framework, due to restrictive measures adopted worldwide by governments to counteract the pandemic contagion. In multi-region areas such as Italy, where the contagion peak has been reached, it is crucial to find targeted and coordinated optimal exit and restarting strategies on a regional basis to effectively cope with possible onset of further epidemic waves, while efficiently returning the economic activities to their standard level of intensity. Differently from the related literature, where modeling and controlling the pandemic contagion is typically addressed on a national basis, this paper proposes an optimal control approach that supports governments in defining the most effective strategies to be adopted during post-lockdown mitigation phases in a multi-region scenario. Based on the joint use of a non-linear Model Predictive Control scheme and a modified Susceptible-Infected-Recovered (SIR)-based epidemiological model, the approach is aimed at minimizing the cost of the so-called non-pharmaceutical interventions (that is, mitigation strategies), while ensuring that the capacity of the network of regional healthcare systems is not violated. In addition, the proposed approach supports policy makers in taking targeted intervention decisions on different regions by an integrated and structured model, thus both respecting the specific regional health systems characteristics and improving the system-wide performance by avoiding uncoordinated actions of the regions. The methodology is tested on the COVID-19 outbreak data related to the network of Italian regions, showing its effectiveness in properly supporting the definition of effective regional strategies for managing the COVID-19 diffusion. © 2020 Elsevier Ltd}, author_keywords={COVID-19; Epidemic control; MPC; Multi-region SIR model; Pandemic modeling; Post-lockdown mitigation strategies; SIR model}, keywords={Economics, Economic activities; Economic framework; Epidemiological modeling; Health-care system; Mitigation strategy; Non-pharmaceutical interventions; Nonlinear model predictive control; Optimal controls, Model predictive control}, references={Alleman, T., Torfs, E., Nopens, I., COVID-19: From model prediction to model predictive control (2020) https://biomath. ugent. be/sites/default/files/2020-04/Alleman_etal_v2. pdf, accessed April, 30, p. 2020; Bai, Y., Yao, L., Wei, T., Tian, F., Jin, D.-Y., Chen, L., Wang, M., Presumed asymptomatic carrier transmission of covid-19 (2020) Jama, 323 (14), pp. 1406-1407; Bertozzi, A.L., Franco, E., Mohler, G., Short, M.B., Sledge, D., The challenges of modeling and forecasting the spread of covid-19 (2020) arXiv preprint arXiv:2004.04741; Bin, M., Cheung, P., Crisostomi, E., Ferraro, P., Myant, C., Parisini, T., Shorten, R., On fast multi-shot epidemic interventions for post lock-down mitigation: Implications for simple covid-19 models (2020) arXiv preprint arXiv:2003.09930; Bin, M., Cheung, P., Crisostomi, E., Ferraro, P., Myant, C., Parisini, T., Shorten, R., On fast multi-shot epidemic interventions for post lock-down mitigation: Implications for simple covid-19 models (2020) arXiv preprint arXiv:2003.09930; Brugnano, L., Iavernaro, F., Zanzottera, P., A multiregional extension of the SIR model, with application to the COVID-19 spread in Italy (2020) Mathematical Methods in the Applied Sciences, , Wiley Online Library; Bussell, E.H., Dangerfield, C.E., Gilligan, C.A., Cunniffe, N.J., Applying optimal control theory to complex epidemiological models to inform real-world disease management (2019) Philosophical Transactions of the Royal Society B, 374 (1776), p. 20180284; Calafiore, G.C., Novara, C., Possieri, C., A modified SIR model for the COVID-19 contagion in Italy (2020) arXiv preprint arXiv:2003.14391; Carli, R., Cavone, G., Dotoli, M., Epicoco, N., Scarabaggio, P., Model predictive control for thermal comfort optimization in building energy management systems (2019) 2019 ieee international conference on systems, man and cybernetics (smc), pp. 2608-2613. , IEEE; Casella, F., Can the COVID-19 epidemic be controlled on the basis of daily test reports? (2021) IEEE Control Systems Letters, 5 (3), pp. 1079-1084; Chen, Z., Discrete-time vs. continuous-time epidemic models in networks (2019) IEEE Access, 7, pp. 127669-127677; Della Rossa, F., Salzano, D., Di Meglio, A., Intermittent yet coordinated regional strategies can alleviate the COVID-19 epidemic: A network model of the Italian case (2020) arXiv preprint arXiv:2005.07594; Di Domenico, L., Pullano, G., Coletti, P., Hens, N., Colizza, V., Expected impact of school closure and telework to mitigate COVID-19 epidemic in France (2020) Technical Report, , Report; Ferguson, N., Laydon, D., Nedjati-Gilani, G., Imai, N., Ainslie, K., Baguelin, M., Cuomo-Dannenburg, G., Report 9: Impact of non-pharmaceutical interventions (npis) to reduce covid19 mortality and healthcare demand (2020) Imperial College London, 10, p. 77482; (2020), https://www.gazzettaufficiale.it/eli/id/2020/04/27/20A02352/sg, Gazzetta Ufficiale Repubblica Italiana, Decree of the President of the Council of Ministers april 26, 2020: urgent measures regarding the containment and management of the COVID-19 epidemiological emergency (in Italian) [Online; accessed 26. Aug. 2020]; Gatto, M., Bertuzzo, E., Mari, L., Miccoli, S., Carraro, L., Casagrandi, R., Rinaldo, A., Spread and dynamics of the covid-19 epidemic in italy: Effects of emergency containment measures (2020) Proceedings of the National Academy of Sciences, 117 (19), pp. 10484-10491; Giordano, G., Blanchini, F., Bruno, R., Colaneri, P., Di Filippo, A., Di Matteo, A., Colaneri, M., Modelling the COVID-19 epidemic and implementation of population-wide interventions in Italy (2020) Nature Medicine, pp. 1-6; Hernandez-Vargas, E.A., Alanis, A.Y., Tetteh, J., A new view of multiscale stochastic impulsive systems for modeling and control of epidemics (2019) Annual Reviews in Control, 48, pp. 242-249; Hethcote, H.W., The mathematics of infectious diseases (2000) SIAM Review, 42, pp. 599-653; Hu, H., Nigmatulina, K., Eckhoff, P., The scaling of contact rates with population density for the infectious disease models (2013) Mathematical biosciences, 244 (2), pp. 125-134; (2020), http://www.salute.gov.it, Italian Ministry of Health, The Italian Ministry of Health website., Accessed: 2020-08-13; (2020), https://www.istat.it/en/information-and-services, Italian Statistics National Institute, The Italian National Institute of Statistics website. [Online: Accessed: 2020-08-13]; http://opendatadpc.maps.arcgis.com/apps/opsdashboard/index.html#/b0c68bce2cce478eaac82fe38d4138b1, Italian Civil Protection Department, The Civil Protection Department COVID-19 dashboard (2020a). [Accessed: 2020-08-13]; http://www.protezionecivile.gov.it/, Italian Civil Protection Department, The Civil Protection Department website, 2020b., Accessed: 2020-08-13; Köhler, J., Schwenkel, L., Koch, A., Berberich, J., Pauli, P., Allgöwer, F., Robust and optimal predictive control of the COVID-19 outbreak (2020) arXiv preprint arXiv:2005.03580; Leung, K., Wu, J.T., Liu, D., Leung, G.M., First-wave covid-19 transmissibility and severity in china outside hubei after control measures, and second-wave scenario planning: a modelling impact assessment (2020) The Lancet; MATLAB, Matlab user guide 9.8.0.135996 (R2020a) (2020), The MathWorks Inc. Natick, Massachusetts; Mei, W., Mohagheghi, S., Zampieri, S., Bullo, F., On the dynamics of deterministic epidemic propagation over networks (2017) Annual Reviews in Control, 44, pp. 116-128; Morato, M.M., Normey-Rico, J.E., Sename, O., Model predictive control design for linear parameter varying systems: A survey (2020) Annual Reviews in Control; Ngonghala, C.N., Iboi, E., Eikenberry, S., Scotch, M., MacIntyre, C.R., Bonds, M.H., Gumel, A.B., Mathematical assessment of the impact of non-pharmaceutical interventions on curtailing the 2019 novel coronavirus (2020) Mathematical Biosciences, p. 108364; Nowzari, C., Preciado, V.M., Pappas, G.J., Analysis and control of epidemics: A survey of spreading processes on complex networks (2016) IEEE Control Systems Magazine, 36 (1), pp. 26-46; Rachah, A., Torres, D.F., Mathematical modelling, simulation, and optimal control of the 2014 ebola outbreak in west africa (2015) Discrete Dynamics in Nature and Society, 2015; Ridenhour, B., Kowalik, J.M., Shay, D.K., Unraveling r 0: Considerations for public health applications (2018) American journal of public health, 108 (S6), pp. S445-S454; Rodrigues, H.S., Monteiro, M.T.T., Torres, D.F., Vaccination models and optimal control strategies to dengue (2014) Mathematical biosciences, 247, pp. 1-12; Google, L.L.C., (2020), http://www.google.com/covid19/mobility, Google COVID-19 Community Mobility Reports., Accessed: 2020-08-13; Sélley, F., Besenyei, Á., Kiss, I.Z., Simon, P.L., Dynamic control of modern, network-based epidemic models (2015) SIAM Journal on applied dynamical systems, 14 (1), pp. 168-187; Silva, C.J., Torres, D.F., Optimal control for a tuberculosis model with reinfection and post-exposure interventions (2013) Mathematical Biosciences, 244 (2), pp. 154-164; Watkins, N.J., Nowzari, C., Pappas, G.J., Robust economic model predictive control of continuous-time epidemic processes (2019) IEEE Transactions on Automatic Control, 65 (3), pp. 1116-1131; Zhao, S., Chen, H., Modeling the epidemic dynamics and control of COVID-19 outbreak in China (2020) Quantitative Biology, 8, pp. 11-19; (2020), https://www.who.int/emergencies/diseases/novel-coronavirus-2019, World Health Organization, Coronavirus disease (COVID-19) pandemic, Accessed: 2020-08-13}, document_type={Article}, source={Scopus}, }`

- Carli, R., Cavone, G., Epicoco, N., Di Ferdinando, M., Scarabaggio, P. & Dotoli, M. (2020) Consensus-Based Algorithms for Controlling Swarms of Unmanned Aerial Vehicles. IN Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12338 LNCS.84-99.

[Bibtex]`@ARTICLE{Carli202084, author={Carli, R. and Cavone, G. and Epicoco, N. and Di Ferdinando, M. and Scarabaggio, P. and Dotoli, M.}, title={Consensus-Based Algorithms for Controlling Swarms of Unmanned Aerial Vehicles}, journal={Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}, year={2020}, volume={12338 LNCS}, pages={84-99}, doi={10.1007/978-3-030-61746-2_7}, note={cited By 3}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85093852465&doi=10.1007%2f978-3-030-61746-2_7&partnerID=40&md5=1c7da6000e4015880227c1eafe608f20}, abstract={Multiple Unmanned Aerial Vehicles (multi-UAVs) applications are recently growing in several fields, ranging from military and rescue missions, remote sensing, and environmental surveillance, to meteorology, logistics, and farming. Overcoming the limitations on battery lifespan and on-board processor capabilities, the coordinated use of multi-UAVs is indeed more suitable than employing a single UAV in certain tasks. Hence, the research on swarm of UAVs is receiving increasing attention, including multidisciplinary aspects, such as coordination, aggregation, network communication, path planning, information sensing, and data fusion. The focus of this paper is on defining novel control strategies for the deployment of multi-UAV systems in a distributed time-varying set-up, where UAVs rely on local communication and computation. In particular, modeling the dynamics of each UAV by a discrete-time integrator, we analyze the main swarm intelligence strategies, namely flight formation, swarm tracking, and social foraging. First, we define a distributed control strategy for steering the agents of the swarm towards a collection point. Then, we cope with the formation control, defining a procedure to arrange agents in a family of geometric formations, where the distance between each pair of UAVs is predefined. Subsequently, we focus on swarm tracking, defining a distributed mechanism based on the so-called leader-following consensus to move the entire swarm in accordance with a predefined trajectory. Moreover, we define a social foraging strategy that allows agents to avoid obstacles, by imposing on-line a time-varying formation pattern. Finally, through numerical simulations we show the effectiveness of the proposed algorithms. © 2020, Springer Nature Switzerland AG.}, author_keywords={Swarm intelligence; Trajectory control; Unmanned Aerial Vehicles}, keywords={Aircraft detection; Antennas; Data fusion; Distributed parameter control systems; Military applications; Military vehicles; Remote sensing; Unmanned aerial vehicles (UAV), Control strategies; Discrete-time integrators; Distributed control strategy; Environmental surveillance; Local communications; Network communications; Onboard processors; Time-varying formations, Mobile ad hoc networks}, references={Bandala, A., Dadios, E., Vicerra, R., Lim, L.G., Swarming algorithm for unmanned aerial vehicle (UAV) quadrotors: Swarm behavior for aggregation, foraging, formation, and tracking (2014) J. Adv. Comput. Intell. Intell. Inform., 18 (5), pp. 745-751; Barabasi, A.L., Taming complexity (2005) Nat. Phys., 1 (2), pp. 68-70; Colorado, J., Barrientos, A., Martinez, A., Lafaverges, B., Valente, J., Mini-quadrotor attitude control based on Hybrid Backstepping & Frenet-Serret theory (2010) Proceedings of the IEEE International Conference on Robotics and Automation (ICRA); Dong, X., Yu, B., Shi, Z., Zhong, Y., Time-varying formation control for unmanned aerial vehicles: Theories and applications (2015) IEEE Trans. Contr. Syst. Techn., 23 (1), pp. 340-348; Dong, X., Zhou, Y., Ren, Z., Zhong, Y., Time-varying formation tracking for second-order multi-agent systems subjected to switching topologies with application to quadrotor formation flying (2016) IEEE Trans. Ind. Electron., 64 (6), pp. 5014-5024; Gazi, V., Passino, K., (2011) Swarm Stability and Optimization, , https://doi.org/10.1007/978-3-642-18041-5, Springer, Heidelberg; Gioioso, G., Franchi, A., Salvietti, G., Scheggi, S., Prattichizzo, D., The flying hand: A formation of UAVs for cooperative aerial telemanipulation (2014) Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), pp. 4335-4341; Gu, Z., Shi, P., Yue, D., Ding, Z., Decentralized adaptive event-triggered h∞ filtering for a class of networked nonlinear interconnected systems (2018) IEEE Trans. Cybern., 49 (5), pp. 1570-1579; Indu, C., Singh, R., Trajectory planning and optimization for UAV communication: A review (2020) J. Discret. Math. Sci. Cryptog., 23 (2), pp. 475-483; Kushleyev, A., Mellinger, D., Powers, C., Kumar, V., Towards a swarm of agile micro quadrotors (2013) Auton. Robots, 35 (4), pp. 287-300; Lee, H., Kim, H., Kim, H., Path planning and control of multiple aerial manipulators for a cooperative transportation (2015) Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS); Low, C., A dynamic virtual structure formation control for fixedwing UAVs (2011) Proceedings of the 9Th IEEE IEEE International Conference on Control and Automation (ICCA), pp. 627-632. , pp; Magnussen, O., Ottestad, M., Hovland, G., Experimental validation of a quaternion-based attitude estimation with direct input to a quadcopter control system (2013) Proceedings of the International Conference on Unmanned Aircraft Systems Unmanned Aircraft Systems (ICUAS); Mellinger, D., Michael, N., Kumar, V., Trajectory generation and control for precise aggressive maneuvers with quadrotors (2014) Exp. Robot., 79, pp. 361-373; Monteiro, S., Bicho, E., A dynamical systems approach to behavior-based formation control (2002) Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), pp. 2606-2611. , pp; Mu, B., Shi, Y., Distributed LQR consensus control for heterogeneous multia-gent systems: Theory and experiments (2018) IEEE/ASME Trans. Mech., 23 (1), pp. 434-443; Nathan, P., Almurib, H., Kumar, T., A review of autonomous multi-agent quadrotor control techniques and applications (2011) Proceedings of 4Th International Conference on Mechatronics (ICOM); Pantelimon, G., Tepe, K., Carriveau, R., Ahmed, S., Survey of multi-agent communication strategies for information exchange and mission control of drone deployments (2019) J. Intell. Robot. Syst., 95, pp. 779-788; Ren, W., Beard, R., (2008) Distributed Consensus in Multi-Vehicle Cooperative Control-Theory and Applications, , https://doi.org/10.1007/978-1-84800-015-5, Springer, London; Roldo, V., Cunha, R., Cabecinhas, D., Silvestre, C., Oliveira, P., A leader-following trajectory generator with application to quadrotor formation flight (2014) Robot. Auton. Syst., 62 (10), pp. 1597-1609; Sa, I., Corke, P., Estimation and control for an open-source quadcopter (2011) Australian Conference of Robotics and Automation (ACRA; Saska, M., Autonomous deployment of swarms of micro-aerial vehicles in cooperative surveillance (2014) Proceedings of the International Conference on Unmanned Aircraft Systems (ICUAS); Saska, M., Vakula, J., Preucil, L., Swarms of micro aerial vehicles stabilized under a visual relative localization (2014) Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), pp. 3570-3575. , pp; Seo, J., Ahn, C., Kim, Y., Controller design for UAV formation flight using consensus based decentralized approach (2009) Proceedings of the AIAA Infotech@Aerospace Conference; Song, Z., Duan, C., Wang, J., Wu, Q., Chattering-free full-order recursive sliding mode control for finite-time attitude synchronization of rigid spacecraft (2019) J. Franklin Inst., 356 (2), pp. 998-1020; Toksoz, M., Oguz, S., Gazi, V., Decentralized formation control of a swarm of quadrotor helicopters (2019) Proceedings of the IEEE 15Th International Conference on Control and Automation (ICCA); Wang, C., Tnunay, H., Zuo, Z., Lennox, B., Ding, Z., Fixed-time formation control of multirobot systems: Design and experiments (2019) IEEE Trans. Ind. Electron., 66 (8), pp. 6292-6301; Wang, C., Zuo, Z., Qi, Z., Ding, Z., Predictor-based extended-state-observer design for consensus of MASs with delays and disturbances (2009) IEEE Trans. Cybern., 49 (4), pp. 1259-1269; Wang, J., Zhou, Z., Wang, C., Shan, J., Multiple quadrotors formation flying control design and experimental verification (2019) Unmanned Syst, 7 (1), pp. 47-54; Zhao, S., Affine formation maneuver control of multi-agent systems (2018) IEEE Trans. Autom. Contr., 63 (12), pp. 4140-4155; Zhao, S., Dimarogonas, D., Sun, Z., Bauso, D., A general approach to coordination control of mobile agents with motion constraints (2018) IEEE Trans. Automat. Contr., 63 (5), pp. 1509-1516}, document_type={Conference Paper}, source={Scopus}, }`

- Carli, R., Digiesi, S., Dotoli, M. & Facchini, F. (2020) A control strategy for smart energy charging of warehouse material handling equipment IN Procedia Manufacturing., 503-510.

[Bibtex]`@CONFERENCE{Carli2020503, author={Carli, R. and Digiesi, S. and Dotoli, M. and Facchini, F.}, title={A control strategy for smart energy charging of warehouse material handling equipment}, journal={Procedia Manufacturing}, year={2020}, volume={42}, pages={503-510}, doi={10.1016/j.promfg.2020.02.041}, note={cited By 5}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084243764&doi=10.1016%2fj.promfg.2020.02.041&partnerID=40&md5=795bb61103f666156bed4ba693a6503f}, abstract={The common driver of the 'green-warehouse' strategy is based on the reduction of energy consumption. In warehouses with 'picker-to-part' operations the minimization of energy due to material handling activities can be achieved by means of different policies: by adopting smart automatic picking systems, by adopting energy-efficient material handling equipment (MHE) as well as by identifying flexible layouts. In most cases, these strategies require investments characterized by high pay-back times. In this context, management strategies focused on the adoption of available equipment allow to increase the warehouse productivity at negligible costs. With this purpose, an optimization model is proposed in order to identify an optimal control strategy for the battery charging of a fleet of electric mobile MHE (e.g., forklifts), allowing minimizing the economic and environmental impact of material handling activities in labor-intensive warehouses. The resulting scheduling problem is formalized as an integer programming (IP) problem aimed at minimizing the total cost, which is the sum of the penalty cost related to makespan over all the material handling activities and the total electricity cost for charging batteries of MHE. Numerical experiments are used to investigate and quantify the effects of integrating the scheduling of electric loads into the scheduling of material handling operations. © 2020 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of the International Conference on Industry 4.0 and Smart Manufacturing.}, author_keywords={Battery smart charging; Green warehouse; Industrial/manufacturing demand side management; Integer programming; Material handling activity; Optimization; Warehouse energy management}, references={Bartolini, M., Bottani, E., Grosse, E.H., Green warehousing: Systematic literature review and bibliometric analysis (2019) Journal of Cleaner Production, 226, pp. 242-258; Dhooma, J., Baker, P., An exploratory framework for energy conservation in existing warehouses (2012) International Journal of Logistics Research and Applications, 15 (1), pp. 37-51; (2016) International Energy Outlook, , https://www.iea.org/, accessed 2 September 2019; Piccinni, G., Avitabile, G., Coviello, G., Talarico, C., Analysis and modeling of a novel SDR-based high-precision positioning system (2018) 2015 Int. Conf. On Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design (SMACD), pp. 13-16; Piccinni, G., Avitabile, G., Coviello, G., A novel distance measurement technique for indoor positioning systems based on Zadoff-Chu Sequences (2017) 2017 15th IEEE International New Circuits and Systems Conference (NEWCAS), pp. 337-340; Gattullo, M., Evangelista, A., Uva, A.E., Fiorentino, M., Boccaccio, A., Manghisi, V.M., Exploiting augmented reality to enhance piping and instrumentation diagrams for information retrieval tasks in industry 4.0 maintenance (2019) Proceedings of the International Conference on Virtual Reality and Augmented Reality, pp. 170-180. , Springer, Cham; Uva, A.E., Fiorentino, M., Gattullo, M., Colaprico, M., de Ruvo, M.F., Marino, F., Trotta, G.F., Monno, G., Design of a projective AR workbench for manual working stations (2016) International Conference on Augmented Reality, Virtual Reality and Computer Graphics, pp. 358-367. , Springer, Cham; Freis, J., Vohlidka, P., Gunthner, W.A., Low-carbon warehousing: Examining impacts of building and intra-logistics design options on energy demand and the CO2 emissions of logistics centers (2016) Sustainability, 8, p. 448; Zhang, Q., Grossmann, I.E., Enterprise-wide optimization for industrial demand side management: Fundamentals, advances, and perspectives (2016) Chemical Engineering Research and Design, 116, pp. 114-131; Moon, J.Y., Park, J., Smart production scheduling with time-dependent and machine-dependent electricity cost by considering distributed energy resources and energy storage (2014) International Journal of Production Research, 52 (13), pp. 3922-3939; Bortolini, M., Faccio, M., Ferrari, E., Gamberi, M., Pilati, F., Time and energy optimal unit-load assignment for automatic S/R warehouses (2017) International Journal of Production Economics, 190, pp. 133-145; Malaguti, E., Nannicini, G., Thomopulos, D., Optimizing allocation in a warehouse network (2018) Electronic Notes in Discrete Mathetatics, 64, pp. 195-204; Ghalehkhondabi, I., Masel, D.T., Storage allocation in a warehouse based on the forklifts fleet availability (2018) Journal of Algorithms & Computational Technology, 12 (2), pp. 127-135; Ene, S., Küçükoglu, I., Aksoy, A., Oztürk, N., A genetic algorithm for minimizing energy consumption in warehouses (2016) Energy, 114, pp. 973-980; Boysen, N., Fedtke, S., Weidinger, F., Optimizing automated sorting in warehouses: The minimum order spread sequencing problem (2018) European Journal of Operational Research, 270 (1), pp. 386-400; Boysen, N., Briskorn, D., Emde, S., Parts-to-picker based order processing in a rack-moving mobile robots environment (2017) European Journal of Operational Research, 262, pp. 550-562; Minav, T.A., Laurila, L.I.E., Immonen, P.A., Haapala, M.E., Pyrhonen, J.J., Electric energy recovery system efficiency in a hydraulic forklift (2009) Proceedings of IEEE EUROCON, pp. 758-765; Minav, T.A., Laurila, L.I.E., Pyrhonen, J.J., Analysis of electro-hydraulic lifting system's energy efficiency with direct electric drive pump control (2013) Automation in Construction, 30, pp. 144-150; Carli, R., Dotoli, M., Decentralized control for residential energy management of a smart users' microgrid with renewable energy exchange IEEE/CAA (2019) Journal of Automatica Sinica, 6 (3), pp. 641-656; Finn, P., Fitzpatrick, C., Demand side management of industrial electricity consumption: Promoting the use of renewable energy through real-time pricing (2014) Applied Energy, 113, pp. 11-21; Gellings, C.W., The concept of demand-side management for electric utilities (1985) Proceedings of the IEEE, 73 (10), pp. 1468-1470; Paulus, M., Borggrefe, F., The potential of demand-side management in energy-intensive industries for electricity markets in Germany (2011) Applied Energy, 88 (2), pp. 432-441; Ramin, D., Spinelli, S., Brusaferri, A., Demand-side management via optimal production scheduling in power-intensive industries: The case of metal casting process (2018) Applied Energy, 225, pp. 622-636; Zhao, S., Ochoa, M.P., Tang, L., Lotero, I., Gopalakrishnan, A., Grossmann, I.E., Novel formulation for optimal schedule with demand side management in multiproduct air separation processes (2019) Industrial & Engeneering Chemistry Research, 58 (8), pp. 3104-3117; Çelebi, E., Fuller, J.D., Time-of-use pricing in electricity markets under different market structures (2012) IEEE Transactions on Power Systems, 27 (3), pp. 1170-1181; Glover, F., Improved linear integer programming formulations of nonlinear integer problems (1975) Management Science, 22 (4), pp. 455-460; Achterberg, T., SCIP: Solving constraint integer programs (2009) Mathematical Programming Computation, 1, pp. 1-41}, document_type={Conference Paper}, source={Scopus}, }`

- Carli, R. & Dotoli, M. (2020) Distributed Alternating Direction Method of Multipliers for Linearly Constrained Optimization over a Network. IN IEEE Control Systems Letters, 4.247-252.

[Bibtex]`@ARTICLE{Carli2020247, author={Carli, R. and Dotoli, M.}, title={Distributed Alternating Direction Method of Multipliers for Linearly Constrained Optimization over a Network}, journal={IEEE Control Systems Letters}, year={2020}, volume={4}, number={1}, pages={247-252}, doi={10.1109/LCSYS.2019.2923078}, art_number={8736857}, note={cited By 4}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85068640431&doi=10.1109%2fLCSYS.2019.2923078&partnerID=40&md5=7f07a37d7f737276c9983542771cba08}, abstract={In this letter we address the distributed optimization problem for a network of agents, which commonly occurs in several control engineering applications. Differently from the related literature, where only consensus constraints are typically addressed, we consider a challenging distributed optimization set-up where agents rely on local communication and computation to optimize a sum of local objective functions, each depending on individual variables subject to local constraints, while satisfying linear coupling constraints. Thanks to the distributed scheme, the resolution of the optimization problem turns into designing an iterative control procedure that steers the strategies of agents-whose dynamics is decoupled-not only to be convergent to the optimal value but also to satisfy the coupling constraints. Based on duality and consensus theory, we develop a proximal Jacobian alternating direction method of multipliers (ADMM) for solving such a kind of linearly constrained convex optimization problems over a network. Using the monotone operator and fixed point mapping, we analyze the optimality of the proposed algorithm and establish its o(1/t) convergence rate. Finally, through numerical simulations we show that the proposed algorithm offers higher computational performances than recent distributed ADMM variants. © 2019 IEEE.}, author_keywords={Distributed control; distributed optimization; optimization algorithms}, keywords={Computation theory; Convex optimization; Iterative methods, Alternating direction method of multipliers; Computational performance; Constrained convex optimizations; Distributed control; Distributed optimization; Engineering applications; Linearly constrained optimization; Optimization algorithms, Constrained optimization}, references={Carli, R., Dotoli, M., A distributed control algorithm for waterfilling of networked control systems via consensus (2017) IEEE Control Syst. Lett., 1 (2), pp. 334-339. , Oct; Olfati-Saber, R., Fax, J.A., Murray, R.M., Consensus and cooperation in networked multi-agent systems (2007) Proc. IEEE, 95 (1), pp. 215-233. , Jan; Tsitsiklis, J.N., (1984) Problems in Decentralized Decision Making and Computation, , DTIC, LIDS, MIT, Cambridge, MA, USA, Rep. LIDS-TH-1424; Tsitsiklis, J.N., Bertsekas, D.P., Athans, M., Distributed asynchronous deterministic and stochastic gradient optimization algorithms (1986) IEEE Trans. Autom. Control, AC-31 (9), pp. 803-812. , Sep; Nedic, A., Ozdaglar, A., Distributed subgradient methods for multiagent optimization (2009) IEEE Trans. Autom. Control, 54 (1), p. 48; Nedic, A., Ozdaglar, A., Parrilo, P.A., Constrained consensus and optimization in multi-agent networks (2010) IEEE Trans. Autom. Control, 55 (4), pp. 922-938. , Apr; Chen, G., Yang, Q., Distributed constrained optimization for multiagent networks with nonsmooth objective functions (2019) Syst. Control Lett., 124, pp. 60-67. , Feb; Boyd, S., Parikh, N., Chu, E., Peleato, B., Eckstein, J., Distributed optimization and statistical learning via the alternating direction method of multipliers (2010) Found. Trends Mach. Learn., 3 (1), pp. 1-122; Makhdoumi, A., Ozdaglar, A., Convergence rate of distributed ADMM over networks (2017) IEEE Trans. Autom. Control, 62 (10), pp. 5082-5095. , Oct; Yu, Y., Açikmeşe, B., Mesbahi, M., Bregman parallel direction method of multipliers for distributed optimization via mirror averaging (2018) IEEE Control Syst. Lett., 2 (2), pp. 302-306. , Apr; Bastianello, N., Todescato, M., Carli, R., Schenato, L., Distributed optimization over lossy networks via relaxed Peaceman-Rachford splitting: A robust ADMM approach (2018) Proc. IEEE Eur. Control Conf., pp. 477-482; Mahey, P., Lenoir, A., A survey on operator splitting and decomposition of convex programs (2017) RAIRO Oper. Res. EDP Sci., 51 (1), pp. 14-41; Hosseini, S., Chapman, A., Mesbahi, M., Online distributed ADMM via dual averaging (2014) Proc. IEEE Int. Conf. Dec. Control, pp. 904-909. , Dec; Chang, T.-H., A proximal dual consensus ADMM method for multiagent constrained optimization (2016) IEEE Trans. Signal Process., 64 (14), pp. 3719-3734. , Jul; Gu, C., Wu, Z., Li, J., Guo, Y., (2018) Distributed Convex Optimization with Coupling Constraints over Time-varying Directed Graphs, , arXiv preprint; Notarnicola, I., Franceschelli, M., Notarstefano, G., A duality-based approach for distributed min-max optimization (2019) IEEE Trans. Autom. Control, 64 (6), pp. 2559-2566. , Jun; Notarnicola, I., Notarstefano, G., A duality-based approach for distributed optimization with coupling constraints (2017) Proc. IFAC World Congr., pp. 14891-14896; Grammatico, S., Dynamic control of agents playing aggregative games with coupling constraints (2017) IEEE Trans. Autom. Control., 62 (9), pp. 4537-4548. , Sep; Carli, R., Dotoli, M., Distributed control for waterfilling of networked control systems with coupling constraints (2018) Proc. IEEE Int. Conf. Dec. Control, pp. 3710-3715; Zhang, Y., Zavlanos, M.M., A consensus-based distributed augmented Lagrangian method (2018) Proc. IEEE Int. Conf. Dec. Control, pp. 1763-1768; Boyd, S., Vandenberghe, L., (2004) Convex Optimization, , Cambridge, U.K.: Cambridge Univ. Press; Deng, W., Lai, M.-J., Peng, Z., Yin, W., Parallel multi-block ADMM with o(1/k) convergence (2017) J. Sci. Comput., 71 (2), pp. 712-736; Berinde, V., (2007) Iterative Approximation of Fixed Points, , Berlin, Germany: Springer; Bauschke, H.H., Combettes, P.L., (2011) Convex Analysis and Monotone Operator Theory in Hilbert Spaces, 408. , New York, NY, USA: Springer; Rockafellar, R.T., Wets, R.J.B., (1998) Variational Analysis, , New York, NY, USA: Springer; Ryu, E.K., Boyd, S., Primer on monotone operator methods (2016) Appl. Comput. Math, 15 (1), pp. 3-43}, document_type={Article}, source={Scopus}, }`

### 2019

- Carli, R., Cavone, G., Dotoli, M., Epicoco, N., Manganiello, C. & Tricarico, L. (2019) ICT-based methodologies for sheet metal forming design: A survey on simulation approaches IN Conference Proceedings – IEEE International Conference on Systems, Man and Cybernetics., 128-133.

[Bibtex]`@CONFERENCE{Carli2019128, author={Carli, R. and Cavone, G. and Dotoli, M. and Epicoco, N. and Manganiello, C. and Tricarico, L.}, title={ICT-based methodologies for sheet metal forming design: A survey on simulation approaches}, journal={Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics}, year={2019}, volume={2019-October}, pages={128-133}, doi={10.1109/SMC.2019.8914082}, art_number={8914082}, note={cited By 0}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85076787727&doi=10.1109%2fSMC.2019.8914082&partnerID=40&md5=b067223578db03937c9be68febc5f920}, abstract={Sheet metal forming processes are widely adopted in manufacturing industries and in the recent years there has been a growing demand for sheet metal items with different shapes and characteristics. However, the traditional process is unable to meet the modern industrial requirements, mainly due to the high costs of dies and the long manufacturing time cycles. On the contrary, developing products with high speed, low cost, and high quality is a key issue. Therefore, new methods and technologies to speed up the sheet metal forming process while keeping costs limited are needed. In particular, a key issue is the proper design of the forming process, which can benefit from the use of Information and Communications Technology (ICT) simulation techniques. This paper investigates the recent trends on ICT-based methodologies for sheet metal forming to identify the foremost research areas whose advancement will lead meeting the modern market's needs. © 2019 IEEE.}, keywords={Costs; Information use; Metal forming; Metals, Developing product; Different shapes; Industrial requirements; Information and communications technology; Manufacturing industries; Manufacturing time; Simulation approach; Simulation technique, Sheet metal}, references={Suchy, I., (2006) Handbook of Die Design, , McGraw-Hill; Vollertsen, F., Schmidt, F., Dry metal forming: Definition, chances and challenges (2014) Int. J. Precis. Eng. Manuf. Technol, 1 (1), pp. 59-62; Mulidrán, P., Spišák, E., Majerníková, J., Springback prediction in sheet metal forming via fea simulation (2017) Int. J. Eng. Sci, 6 (9), pp. 49-52; Geiger, M., Merklein, M., Kerausch, M., Finite element simulation of deep drawing of tailored heat treated blanks (2004) CIRP Ann, 53 (1), pp. 223-226; Ingarao, G., Di Lorenzo, R., Design of complex sheet metal forming processes: A new computer aided progressive approach (2010) Int. J. Mater. Form, 3 (1), pp. 21-24; (1998) Schuler GmbH., Metal Forming Handbook, , Springer Berlin Heidelberg; Ramezani, M., Ripin, Z.M., Forming of shallow parts using rubber tools (2012) Rubber-Pad Form. Process, pp. 65-102; Reddy, P., Reddy, G., Prasad, P., A review on finite element simulations in metal forming (2012) Int. J. Mod. Eng. Res, 2 (4), pp. 2326-2330; Zhou, D., Knowledge based cloud fe simulation of sheet metal forming processes (2016) J. Vis. Exp, 118; LS-DYNA, , www.ansys.com/en-gb/products/structures/ansys-ls-dyna, [Accessed: 18-Apr-2019]; AutoForm, , www.autoform.com/en/products/solution-overview, [Accessed: 18-Apr-2019]; Abaqus, , www.3ds.com/products-services/simulia/products/abaqus, [Accessed: 18-Apr-2019]; Pam-Stamp, , www.esi.com.au/software/pamstamp, [Accessed: 18-Apr-2019]; DeGarmo, E.P., Black, J.T., Kohser, R.A., (2017) Materials and Processes in Manufacturing, , Wiley; Ashby, M.F., Shercliff, H., Cebon, D., (2018) Materials: Engineering, Science, Processing and Design, , Elsevier; Iorio, L., Strano, M., Monno, M., Development of a die compensation algorithm for sheet metal stamping with deformable tools (2015) Proc. ASME 2015 International Manufacturing Science and Engineering Conference; Vrolijk, M., Ogawa, T., Camanho, A., Biasutti, M., Lorenz, D., A study with esi pam-stamp® on the influence of tool deformation on final part quality during a forming process (2018) AIP Conference Proceedings, 1960 (1); Cafolla, J., Hall, R.W., Norman, D.P., McGregor, J., Forming to crash' simulation in full vehicle models (2003) 4th European LS-DYNA Users Conference; Wang, W.R., Chen, G.L., Lin, Z.Q., Li, S.H., Determination of optimal blank holder force trajectories for segmented binders of step rectangle box using pid closed-loop fem simulation (2007) Int. J. Adv. Manuf. Technol, 32 (11-12), pp. 1074-1082; Wang, Y., Liu, D.-Z., Li, R., Numerical investigation for the flexible stretch-stamp forming process of sheet metal (2019) Adv. Mech. Eng, 11 (1), pp. 1-11; Slota, J., Jurišin, M., Springback prediction in sheet metal forming processes (2012) J. Technol. Plast, 37 (1), pp. 93-103; Mulidrán, P., Šiser, M., Slota, J., Spišák, E., Sleziak, T., Numerical prediction of forming car body parts with emphasis on springback (2018) Metals, 8 (6); Strano, M., Optimization under uncertainty of sheet-metal-forming processes by the finite element method (2006) Proc. Inst. Mech. Eng. Part B J. Eng. Manuf, 220 (8), pp. 1305-1315; Shaikh, A.M.G., Rao, T.B., Sheet metal forming simulations for heavy commercial vehicle parts by ls-dyna (2013) Glob. J. Res. Eng. Automot. Eng, 13 (1), pp. 35-40; Koreek, D., Solfronk, P., Sobotka, J., Kolnerová, M., Determination the influence of load-rate on strain and spring-back magnitude for titanium alloy by means of numerical simulation (2019) Manuf. Technol, 19 (1), pp. 82-88; Hochholdinger, B., Grass, H., Lipp, A., Hora, P., Determination of flow curves by stack compression tests and inverse analysis for the simulation of hot forming (2009) 7th European LS-DYNA Conference; Papadakis, L., Schober, A., Zaeh, M.F., Considering manufacturing effects in automotive structural crashworthiness: A simulation chaining approach (2013) Int. J. Crashworthiness, 18 (3), pp. 276-287; Jadhav, S., Schoiswohl, M., Buchmayr, B., Applications of finite element simulation in the development of advanced sheet metal forming processes (2018) BHM Berg-und Hüttenmännische Monatshefte, 163 (3), pp. 109-118; Mohamed, M., Norman, D., Petre, A., Melotti, F., Szegda, D., Advances in fem simulation of hfq® aa6082 tailor welded blanks for automotive applications (2018) IOP Conf. Ser. Mater. Sci. Eng, 418 (1), pp. 1-8; Karbasian, H., Tekkaya, A.E., A review on hot stamping (2010) J. Mater. Process. Technol, 210, pp. 2103-2118; Kuhn, H., Medlin, D., (2000) Mechanical Testing and Evaluation, , ASM International; Mosterman, P.J., Zander, J., Industry 4. 0 as a cyber-physical system study (2016) Softw. Syst. Model, 15 (1), pp. 17-29}, document_type={Conference Paper}, source={Scopus}, }`

- Carli, R., Cavone, G., Dotoli, M., Epicoco, N. & Scarabaggio, P. (2019) Model predictive control for thermal comfort optimization in building energy management systems IN Conference Proceedings – IEEE International Conference on Systems, Man and Cybernetics., 2608-2613.

[Bibtex]`@CONFERENCE{Carli20192608, author={Carli, R. and Cavone, G. and Dotoli, M. and Epicoco, N. and Scarabaggio, P.}, title={Model predictive control for thermal comfort optimization in building energy management systems}, journal={Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics}, year={2019}, volume={2019-October}, pages={2608-2613}, doi={10.1109/SMC.2019.8914489}, art_number={8914489}, note={cited By 7}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85076778873&doi=10.1109%2fSMC.2019.8914489&partnerID=40&md5=3c982fb93adbcfb5202b48b60ad0f22d}, abstract={Model Predictive Control (MPC) has recently gained special attention to efficiently regulate Heating, Ventilation and Air Conditioning (HVAC) systems of buildings, since it explicitly allows energy savings while maintaining thermal comfort criteria. In this paper we propose a MPC algorithm for the on-line optimization of both the indoor thermal comfort and the related energy consumption of buildings. We use Fanger's Predicted Mean Vote (PMV) as thermal comfort index, while to predict the energy performance of the building, we adopt a simplified thermal model. This allows computing optimal control actions by defining and solving a tractable non-linear optimization problem that incorporates the PMV index into the MPC cost function in addition to a term accounting for energy saving. The proposed MPC approach is implemented on a building automation system deployed in an office building located at the Polytechnic of Bari (Italy). Several on-field tests are performed to assess the applicability and efficacy of the control algorithm in a real environment against classical thermal comfort control approach based on the use of thermostats. © 2019 IEEE.}, keywords={Air conditioning; Automation; Cost functions; Energy conservation; Energy management systems; Energy utilization; Intelligent buildings; Nonlinear programming; Office buildings; Predictive control systems; Thermal comfort, Building automation systems; Energy performance; Indoor thermal comfort; Non-linear optimization problems; Online optimization; Predicted mean vote; Thermal comfort control; Thermal comfort index, Model predictive control}, references={Dean, B.P., Dulac, J., Petrichenko, K., Graham, Towards a zeroemission, efficient, and resilient buildings and construction sector (2016) Global Status Report; Ranieri, L., Mossa, G., Pellegrino, R., Digiesi, S., Energy recovery from the organic fraction of municipal solid waste: A real optionsbased facility assessment (2018) Sustain, 10 (2), p. 368. , Jan; Jouhara, H., Yang, J., Energy efficient hvac systems (2018) Energy and Buildings, 179, pp. 83-85; Piccinni, G., Avitabile, G., Coviello, G., Talarico, C., Distributed amplifier design for UWB positioning systems using the gm over id methodology (2016) 2016 13th International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design, SMACD 2016; Shaikh, P.H., Nor, N.B.M., Nallagownden, P., Elamvazuthi, I., Ibrahim, T., A review on optimized control systems for building energy and comfort management of smart sustainable buildings (2014) Renewable and Sustainable Energy Reviews, 34, pp. 409-429; Ma, Y., Borrelli, F., Hencey, B., Coffey, B., Bengea, S., Haves, P., Model predictive control for the operation of building cooling systems (2012) IEEE Trans. Control Syst. Technol, 20 (3), pp. 796-803; Serale, G., Fiorentini, M., Capozzoli, A., Bernardini, D., Bemporad, A., Model predictive control (mpc) for enhancing building and hvac system energy efficiency: Problem formulation, applications and opportunities (2018) Energies, 11 (3); Hazyuk, I., Ghiaus, C., Penhouet, D., Optimal temperature control of intermittently heated buildings using model predictive control: Part II-control algorithm (2012) Build. Environ, 51, pp. 388-394; Klauo, M., Drgoa, J., Kvasnica, M., Di Cairano, S., Building temperature control by simple mpc-like feedback laws learned from closed-loop data (2014) IFAC Proceedings Volumes (IFACPapersOnline), 19, pp. 581-586; Klauco, M., Kvasnica, M., Explicit mpc approach to pmv-based thermal comfort control (2014) Proceedings of the IEEE Conference on Decision and Control, pp. 4856-4861. , 2015-Febru February; (2005) ISO 7730: Ergonomics of the Thermal Environment-Analytical Determination and Interpretation of Thermal Comfort Using Calculation of the PMV and PPD Indices and Local Thermal Comfort Criteria, p. 60. , International Organization for Standardization, ISO Stand; Fanger, P.O., (1970) Thermal Comfort: Analysis and Application in Environment Engineering; Afram, A., Janabi-Sharifi, F., Theory and applications of hvac control systems-a review of model predictive control (mpc) (2014) Building and Environment, 72; Xu, Z., Hu, G., Spanos, C.J., Schiavon, S., Pmv-based eventtriggered mechanism for building energy management under uncertainties (2017) Energy Build, 152, pp. 73-85; West, S.R., Ward, J.K., Wall, J., Trial results from a model predictive control and optimisation system for commercial building hvac (2014) Energy Build, 72, pp. 271-279; Alamin, Y.I., Del Mar Castilla, M., Álvarez, J.D., Ruano, A., An economic model-based predictive control to manage the users' thermal comfort in a building (2017) Energies, 10 (3); Cigler, J., Prívara, S., Váa, Z., Žáeková, E., Ferkl, L., Optimization of predicted mean vote index within model predictive control framework: Computationally tractable solution (2012) Energy Build, 52, pp. 39-49. , Sep; Corbin, C.D., Henze, G.P., May-Ostendorp, P., A model predictive control optimization environment for real-time commercial building application (2013) J. Build. Perform. Simul, 6 (3), pp. 159-174; Ascione, F., Bianco, N., De Stasio, C., Mauro, G.M., Vanoli, G.P., Simulation-based model predictive control by the multi-objective optimization of building energy performance and thermal comfort (2016) Energy Build, 111, pp. 131-144; Pippia, T., Sijs, J., De Schutter, B., A parametrized model predictive control approach for microgrids (2019) Proceedings of the IEEE Conference on Decision and Control, pp. 3171-3176. , 2018-Decem; García, C.E., Prett, D.M., Morari, M., Model predictive control: Theory and practice-a survey (1989) Automatica, 25 (3), pp. 335-348; Farina, M., Betti, G., Scattolini, R., A solution to the tracking problem using distributed predictive control (2018) 2013 European Control Conference (ECC), pp. 4347-4352; Beeta, , https://www.beeta.it/en/, [Accessed: 24-Apr-2019]; Node-RED : User Guide, , https://nodered.org/docs/user-guide/, [Accessed: 24-Apr-2019]; Singh, M., Rajan, M.A., Shivraj, V.L., Balamuralidhar, P., Secure mqtt for internet of things (iot) (2015) Proceedings-2015 5th International Conference on Communication Systems and Network Technologies, CSNT 2015, pp. 746-751}, document_type={Conference Paper}, source={Scopus}, }`

- Hosseini, S. M., Carli, R. & Dotoli, M. (2019) A residential demand-side management strategy under nonlinear pricing based on robust model predictive control IN Conference Proceedings – IEEE International Conference on Systems, Man and Cybernetics., 3243-3248.

[Bibtex]`@CONFERENCE{Hosseini20193243, author={Hosseini, S.M. and Carli, R. and Dotoli, M.}, title={A residential demand-side management strategy under nonlinear pricing based on robust model predictive control}, journal={Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics}, year={2019}, volume={2019-October}, pages={3243-3248}, doi={10.1109/SMC.2019.8913892}, art_number={8913892}, note={cited By 18}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85076744329&doi=10.1109%2fSMC.2019.8913892&partnerID=40&md5=b8658f66ca7439fa6e7d9b02705e1b91}, abstract={This paper presents a real-time demand side management framework based on robust model predictive control (RMPC) for residential smart grids. The system incorporates a number of interconnected smart homes, each equipped with controllable and non-controllable loads, as well as a shared energy storage system (ESS). We aim at minimizing the users' energy payment and limiting the peak-to-average ratio (PAR) of the energy consumption while taking into account all device/comfort/contractual constraints, specifically the feasibility constraints on energy transferred between users and the power grid in presence of load demand uncertainty. We consider a quadratic cost function for energy bought from the grid. Firstly, the energy price and related constraints of the system are modeled. Then, a min-max robust problem is established to optimally schedule energy under an interval-based uncertainty set. We finally adopt model predictive control (MPC) to solve the resulting robust optimization problem iteratively over a finite-horizon time window based on the receding horizon concept. Moreover, the robustness of the proposed real-time approach against the level of conservativeness of the solution is addressed. The effectiveness of the method is validated through a simulated case study. © 2019 IEEE.}, keywords={Automation; Cost functions; Costs; Demand side management; Electric power transmission networks; Electric utilities; Energy utilization; Housing; Intelligent buildings; Iterative methods; Optimization; Predictive control systems; Robust control, Controllable loads; Energy storage systems; Non-linear pricing; Peak to average ratios; Quadratic cost functions; Robust model predictive control; Robust model predictive controls (RMPC); Robust optimization, Model predictive control}, references={Ahmed, N., Levorato, M., Li, G.P., Residential consumer-centric demand side management (2018) IEEE Trans. Smart Grid, 9, pp. 4513-4524; Saleh, S.A., Pijnenburg, P., Castillo-Guerra, E., Load aggregation from generation-follows-load to load-follows-generation: Residential loads (2017) IEEE Trans. Ind. Appl, 53, pp. 833-842; Facchini, F., Mummolo, G., Mossa, G., Digiesi, S., Boenzi, F., Verriello, R., Minimizing the carbon footprint of material handling equipment: Comparison of electric and lpg forklifts (2016) Journal of Industrial Engineering and Management, 9 (5), pp. 1035-1046; D'Amato, G., Avitabile, G., Coviello, G., Talarico, C., Toward a novel architecture for beam steering of active phased-array antennas (2016) 2016 IEEE 59th International Midwest Symposium on Circuits and Systems (MWSCAS), pp. 1-4. , October; Talarico, C., D'Amato, G., Coviello, G., Avitabile, G., A high precision phase control unit for dds-based plls for 2. 4-ghz ism band applications (2015) 2015 IEEE 58th International Midwest Symposium on Circuits and Systems (MWSCAS), pp. 1-4. , August; Digiesi, S., Mossa, G., Mummolo, G., Supply lead time uncertainty in a sustainable order quantity inventory model (2013) Management and Production Engineering Review, 4 (4), pp. 15-27; Kuzlu, M., Score-based intelligent home energy management (hem) algorithm for demand response applications and impact of hem operation on customer comfort (2015) IET Gener. Transm. Distrib, 9, pp. 627-635; Chen, C., Duan, S., Cai, T., Liu, B., Hu, G., Smart energy management system for optimal microgrid economic operation (2011) IET Renew. Power Gener, 5, pp. 258-267; Carli, R., Dotoli, M., Decentralized control for residential energy management of a smart users' microgrid with renewable energy exchange (2019) IEEE/CAA Journal of Automatica Sinica, 6 (3), pp. 641-656. , May; Mohsenian-Rad, A., Wong, V.W.S., Jatskevich, J., Schober, R., Optimal and autonomous incentive-based energy consumption scheduling algorithm for smart grid (2010) IEEE Innov. Smart Grid Tech; Yue, J., Hu, Z., Anvari-Moghaddam, A., Guerrero, J.M., A multimarket-driven approach to energy scheduling of smart microgrids in distribution networks (2019) Sustainability, 11, pp. 1-16; Kim, B., Zhang, Y., Schaar Der Van, M., Lee, J., Dynamic pricing and energy consumption scheduling with reinforcement learning (2016) IEEE Trans. Smart Grid, 7, pp. 2187-2198; Ma, J., Chen, H.H., Song, L., Li, Y., Residential load scheduling in smart grid: A cost efficiency perspective (2016) IEEE Trans. Smart Grid, 7, pp. 771-784; Carli, R., Dotoli, M., Epicoco, N., Cost-optimal energy scheduling of a smart home under uncertainty (2018) IEEE Conf. Control Technology and Applications; Safdarian, A., Fotuhi-Firuzabad, M., Lehtonen, M., A stochastic framework for short-term operation of a distribution company (2013) IEEE Trans. Power Systems, 28, pp. 4712-4721; Ghasemi, A., Banejad, M., Rahimiyan, M., Integrated energy scheduling under uncertainty in a micro energy grid (2018) IET Gener. Transm. Distrib, 12, pp. 2887-2896; Chen, Z., Wu, L., Fu, Y., Real-time price-based demand response management for residential appliances via stochastic optimization and robust optimization (2012) IEEE Trans. Smart Grid, 3, pp. 1822-1831; Zhang, Y., Fu, L., Zhu, W., Bao, X., Liu, C., Robust model predictive control for optimal energy management of island microgrids with uncertainties (2018) Energy, Elsevier, 164, pp. 1229-1241; Hosseini, S.M., Carli, R., Dotoli, M., Model predictive control for real-time residential energy scheduling under uncertainties (2018) IEEE Int. Conf. Systems, Man, and Cybernetics; Zhai, M., Liu, Y., Zhang, T., Zhang, Y., Robust model predictive control for energy management of isolated microgrids (2017) IEEE Int. Conf. Industrial Engineering and Engineering Management; Xiang, Y., Liu, J., Liu, Y., Robust energy management of microgrid with uncertain renewable generation and load (2016) IEEE Trans. Smart Grid, 7, pp. 1034-1043; Elghali, S.B., Outbib, R., Benbouzid, M., Selecting and optimal sizing of hybridized energy storage systems for tidal energy integration into power grid (2019) Journal of Modern Power Systems and Clean Energy, 7 (1), pp. 113-122; Bertsimas, D., Sim, M., The price of robustness (2014) Oper. Res, 52, pp. 35-53; Hosseini, S.M., Carli, R., Dotoli, M., Robust energy scheduling of interconnected smart homes with shared energy storage under quadratic pricing (2019) IEEE Conf. Automation Science and Engineering; Cavone, G., Blenkers, L., Boom Den Van, T., Dotoli, M., Seatzu, C., De Schutter, B., Railway disruption: A bi-level rescheduling algorithm (2019) International Conference on Control Decision and Information Technologies; Pippia, T., Sijs, J., De Schutter, B., A parametrized model predictive control approach for microgrids (2018) IEEE Conf. Dec. Contr, pp. 3171-3176}, document_type={Conference Paper}, source={Scopus}, }`

- Hosseini, S. M., Carli, R. & Dotoli, M. (2019) Robust energy scheduling of interconnected smart homes with shared energy storage under quadratic pricing IN IEEE International Conference on Automation Science and Engineering., 966-971.

[Bibtex]`@CONFERENCE{Hosseini2019966, author={Hosseini, S.M. and Carli, R. and Dotoli, M.}, title={Robust energy scheduling of interconnected smart homes with shared energy storage under quadratic pricing}, journal={IEEE International Conference on Automation Science and Engineering}, year={2019}, volume={2019-August}, pages={966-971}, doi={10.1109/COASE.2019.8843230}, art_number={8843230}, note={cited By 3}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85072987292&doi=10.1109%2fCOASE.2019.8843230&partnerID=40&md5=6989d41f45c9a005b8a3dd9ebcf6bbfd}, abstract={In this paper, we propose a novel robust framework for day-ahead energy scheduling of interconnected smart homes with shared energy storage system (ESS), taking into account users' behavior uncertainty. The objective is minimizing the total energy payment for each user while satisfying the constraint on the feasibility of energy transactions between users and the power grid in presence of data uncertainty. Unlike most existing robust scheduling frameworks that assume a linear cost function for energy purchased from the grid, our design presents a tractable robust optimization scheme to solve the energy scheduling problem with a more realistic quadratic cost function. We model device/comfort constraints as well as contractual obligations imposed by the power grid restricting the users' energy consumption to a maximum level at each time slot. Thus, in our problem, uncertainty affects both the quadratic objective function and linear contractual constraints. To solve the resulting problem, we first formulate a deterministic model of the scheduling problem, then establish a min-max robust counterpart, and finally apply some mathematical transformations to solve the equivalent problem. We also deal with the conservatism of the robust control algorithm and flexibility of the method for application to different settings. The validity and effectiveness of the proposed approach is verified by simulation results. © 2019 IEEE.}, keywords={Automation; Cost functions; Digital storage; Electric power transmission networks; Energy storage; Energy utilization; Intelligent buildings; Mathematical transformations; Optimization; Robust control; Scheduling, Contractual obligations; Deterministic modeling; Energy storage systems; Linear cost functions; Quadratic cost functions; Quadratic objective functions; Robust optimization; Scheduling problem, Costs}, references={Cao, X., Dai, X., Liu, J., Building energy-consumption status worldwide and the state-of-The-art technologies for zero-energy buildings during the past decade (2016) Energy AndBuildings, 128, pp. 198-213; Piccinni, G., Avitabile, G., Coviello, G., An improved technique based on Zadoff-Chu sequences for distance measurements (2016) IEEE Radio and Antenna Days of the Indian Ocean, pp. 1-2; Kim, B., Zhang, Y., Van Der Schaar, M., Lee, J., Dynamic pricing and energy consumption scheduling with reinforcement learning (2016) IEEE Trans. Smart Grid, 7 (5), pp. 2187-2198; Mohsenian-Rad, A., Wong, V.W.S., Jatskevich, J., Schober, R., Optimal and autonomous incentive-based energy consumption scheduling algorithm for smart grid (2010) IEEEInnov. Smart Grid Tech.; Carli, R., Dotoli, M., Energy scheduling of a smart home under nonlinear pricing (2014) IEEE Conf. Dec. Contr, pp. 5648-5653; Anvari-Moghaddam, A., Guerrero, J.M., Vasquez, J.C., Monsef, H., Rahimi-Kian, A., Efficient energy management for a grid-tied residential microgrid (2017) IETGener. Transm. Distrib, 11 (11), pp. 2752-2761; Pippia, T., Sijs, J., De Schutter, B., A parametrized model predictive control approach for microgrids (2018) IEEE Conf. Dec. Contr., pp. 3171-3176; Carli, R., Dotoli, M., Decentralized control for residential energy management of a smart users' microgrid with renewable energy exchange (2019) IEEE/CAA Journal of Automatica Sinica, 6 (3), pp. 641-656; Facchini, F., Mummolo, G., Mossa, G., Digiesi, S., Boenzi, F., Verriello, R., Minimizing the carbon footprint of material handling equipment: Comparison of electric and lpg forklifts (2016) Journal OfIndustrialEngineering AndManagement, 9 (5), pp. 1035-1046; Hosseini, S.M., Carli, R., Dotoli, M., Model predictive control for realtime residential energy scheduling under uncertainties (2018) Proc. IEEE Int. Conf. Syst. Man. Cyb, pp. 1386-1391; Safdarian, A., Fotuhi-Firuzabad, M., Lehtonen, M., A stochastic framework for short-term operation of a distribution company (2013) IEEE Trans. Power Systems, 28 (4), pp. 4712-4721; Ghasemi, A., Banejad, M., Rahimiyan, M., Integrated energy scheduling under uncertainty in a micro energy grid (2018) IET Gener. Transm. Distrib, 12 (12), pp. 2887-2896; Chen, Z., Wu, L., Fu, Y., Real-time price-based demand response management for residential appliances via stochastic optimization and robust optimization (2012) IEEE Trans. Smart Grid, 3, pp. 1822-1831; Zhang, C., Xu, Y., Dong, Z.Y., Ma, J., Robust operation of microgrids via two-stage coordinated energy storage and direct load control (2017) IEEE Trans. Power Syst, 32 (4), pp. 2858-2868; Yi, W., Zhang, Y., Zhao, Z., Huang, Y., Multiobjective robust scheduling for smart distribution grids: Considering renewable energy and demand response uncertainty (2018) IEEEAccess, 6, pp. 45715-45724; Wang, C., Zhou, Y., Jiao, B., Wang, D., Robust optimization for load scheduling of a smart home with photovoltaic system (2015) Energy Conversion and Management, 102, pp. 247-257; Hussain, A., Bui, V., Kim, H., Robust optimal operation of ac/dc hybrid microgrids under market price uncertainties (2018) IEEE Access, 6, p. 26542667; Paridari, K., Parisio, A., Sandberg, H., Johansson, K.H., Robust scheduling of smart appliances in active apartments with user behavior uncertainty (2016) IEEETrans. Autom. Sci. Eng, 13 (1), pp. 247-259; Bertsimas, D., Sim, M., The price of robustness (2014) Oper. Res, 52 (1), pp. 35-53; Elghali, S.B., Outbib, R., Benbouzid, M., Selecting and optimal sizing of hybridized energy storage systems for tidal energy integration into power grid (2019) Journal of Modern Power Systems and Clean Energy, 7 (1), pp. 113-122; Samadi, P., Schober, R., Wong, V.W.S., Optimal energy consumption scheduling using mechanism design for the future smart grid (2011) IEEE Smart Grid Commun., pp. 369-374}, document_type={Conference Paper}, source={Scopus}, }`

- Carli, R., Dotoli, M. & Palmisano, V. (2019) A distributed control approach based on game theory for the optimal energy scheduling of a residential microgrid with shared generation and storage IN IEEE International Conference on Automation Science and Engineering., 960-965.

[Bibtex]`@CONFERENCE{Carli2019960, author={Carli, R. and Dotoli, M. and Palmisano, V.}, title={A distributed control approach based on game theory for the optimal energy scheduling of a residential microgrid with shared generation and storage}, journal={IEEE International Conference on Automation Science and Engineering}, year={2019}, volume={2019-August}, pages={960-965}, doi={10.1109/COASE.2019.8843141}, art_number={8843141}, note={cited By 4}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85072980491&doi=10.1109%2fCOASE.2019.8843141&partnerID=40&md5=52d9b8791bf4c74bacd4b6c99e48e8b9}, abstract={This paper presents a distributed control approach based on game theory for the energy scheduling of demand-side consumers sharing energy production and storage while purchasing further energy from the grid. The interaction between the controllers of consumers' loads and the manager of shared energy resources is modeled as a two-level game. The competition among consumers is formulated as a noncooperative game, while the interaction between the consumers' loads and the shared resources manager is formulated as a cooperative game. optimization problems are stated for each player to determine their own optimal strategies. The algorithms for loads controllers and shared resources' manager are implemented through a distributed approach. Numerical experiments show the effectiveness of the proposed scheme. © 2019 IEEE.}, keywords={Controllers; Energy resources; Managers; Scheduling, Distributed approaches; Distributed control; Energy productions; Noncooperative game; Numerical experiments; Optimal strategies; Optimization problems; Residential microgrid, Game theory}, references={Vytelingum, P., Voice, T.D., Ramchurn, S.D., Rogers, A., Jennings, N.R., Agent-based micro-storage management for the smart grid (2010) Proc. Int. Conf. AAMAS, 1, pp. 39-46. , May; Talarico, C., D'Amato, G., Coviello, G., Avitabile, G., A high precision phase control unit for DDS-based PLLs for 2.4-GHz ISM band applications (2015) 2015 IEEE 58th International Midwest Symposium on Circuits and Systems (MWSCAS), pp. 1-4. , , August; Atzeni, I., Ordonez, L.G., Scutari, G., Palomar, D.P., Fonollosa, J.R., Demand-side management via distributed energy generation and storage optimization (2013) IEEE Trans. Smart Grid, 4 (2), pp. 866-876. , Jun; Barbato, A., Capone, A., Optimization models and methods for demand-side management of residential users: A survey (2014) Energies, 7 (9), pp. 5787-5824; Carli, R., Dotoli, M., A decentralized resource allocation approach for sharing renewable energy among interconnected smart homes (2015) IEEE Int. Conf. Dec. Contr, , Dec. 15-18; Carli, R., Dotoli, M., Cooperative distributed control for the energy scheduling of smart homes with shared energy storage and renewable energy source (2017) IFAC WC, 50 (1), pp. 8867-8872. , Jul. 9-14, IFAC-PapersOnLine; Paridari, K., Parisio, A., Sandberg, H., Johansson, K.H., Demand response for aggregated residential consumers with energy storage sharing (2015) Proc. IEEE Int. Conf. Dec. Contr, pp. 2024-2030; Mediwaththe, C.P., Stephens, E.R., Smith, D.B., Mahanti, A., A dynamic game for electricity load management in neighborhood area networks (2016) IEEE Trans. Smart Grid, 7 (3), p. 13291336; Digiesi, S., Mossa, G., Mummolo, G., Supply lead time uncertainty in a sustainable order quantity inventory model (2013) Management and Production Engineering Review, 4 (4), pp. 15-27; Xu, B., Shi, Y., Kirschen, D.S., Zhang, B., Optimal regulation response of batteries under cycle aging mechanisms (2017) Proc. IEEE Int. Conf. Dec. Contr., pp. 751-756; Mohsenian-Rad, A.-H., Wong, V., Jatskevich, J., Schober, R., Leon-Garcia, A., Autonomous demand-side management based on game-theoretic energy consumption scheduling for the future smart grid (2010) IEEE Trans. Smart Grid, 1 (3), pp. 320-331; Samadi, P., Mohsenian-Rad, A.H., Schober, R., Wong, V.W., Jatskevich, J., Optimal real-time pricing algorithm based on utility maximization for smart grid (2010) IEEE Int. Conf. On Smart Grid Communications, pp. 415-420; Brandenburger, A., Stuart, H., Biform games (2007) Management Science, 53 (4), pp. 537-549; Gilles, R.P., (2010) The Cooperative Game Theory of Networks and Hierarchies, 44. , Springer Science &Business Media; Basqr, T., Olsder, G.J., (1999) Dynamic Noncooperative Game Theory, Ser. SIAM Series in Classics in Applied Mathematics, , Philadelphia, PA: SIAM; Boyd, S., Vandenberghe, L., (2004) Convex Optimization, , Cambridge University Press, UK; Facchinei, F., Pang, J.-S., (2003) Finite-Dimensional Variational Inequalities and Complementarity Problem, , New York, NY, USA: Springer-Verlag; Pilz, M., Al-Fagih, L., Pfluegel, E., Energy storage scheduling with an advanced battery model: A game-theoretic approach (2017) Inventions, 2 (4), p. 30; Chai, B., Chen, J., Yang, Z., Zhang, Y., Demand response management with multiple utility companies: A two-level game approach (2014) IEEE Trans. Smart Grid, 5 (2), pp. 722-731; Gao, B., Zhang, W., Tang, Y., Hu, M., Zhu, M., Zhan, H., Game-theoretic energy management for residential users with dischargeable plug-in electric vehicles (2014) Energies, 7 (11), pp. 7499-7518; Tushar, W., Zhang, J.A., Smith, D.B., Poor, H.V., Thiebaux, S., Prioritizing consumers in smart grid: A game theoretic approach (2014) IEEE Trans. Smart Grid, 5 (3), pp. 1429-1438}, document_type={Conference Paper}, source={Scopus}, }`

- Hosseini, S. M., Carli, R. & Dotoli, M. (2019) Robust day-ahead energy scheduling of a smart residential user under uncertainty IN 2019 18th European Control Conference, ECC 2019., 935-940.

[Bibtex]`@CONFERENCE{Hosseini2019935, author={Hosseini, S.M. and Carli, R. and Dotoli, M.}, title={Robust day-ahead energy scheduling of a smart residential user under uncertainty}, journal={2019 18th European Control Conference, ECC 2019}, year={2019}, pages={935-940}, doi={10.23919/ECC.2019.8796182}, art_number={8796182}, note={cited By 19}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85071523023&doi=10.23919%2fECC.2019.8796182&partnerID=40&md5=dbaf920bef232ca07b714d294ec35e28}, abstract={This paper develops a robust optimization framework for the day-ahead energy scheduling of a grid-connected residential user. The system incorporates a renewable energy source (RES), a battery energy storage system (BESS) as well as elastic controllable and critical noncontrollable electrical appliances. The proposed approach copes with the fluctuation and intermittence of the RES generation and non-controllable load demand by a tractable robust optimization scheme requiring minimum information on the sources of uncertainty. The main objective is minimizing the total energy payment for the user considering operational/technical constraints and a contractual constraint penalizing the excessive use of energy. The presented framework allows the decision maker to define different robustness levels for uncertain variables, and to flexibly establish an equilibrium between user's payment and price of robustness. To validate the effectiveness of the proposed framework under uncertainty, we simulate the dynamics of a residential user as a case study. A comparison between the proposed robust approach and the same method with deterministic RES and loads profiles is carried out and discussed. © 2019 EUCA.}, keywords={Decision making; Optimization; Renewable energy resources; Robustness (control systems); Scheduling, Battery energy storage systems; Controllable loads; Electrical appliances; Minimum information; Renewable energy source; Robust optimization; Sources of uncertainty; Uncertain variables, Housing}, references={Samadi, P., Mohsenian-Rad, H., Wong, V.W.S., Schober, R., Tackling the load uncertainty challenges for energy consumption scheduling in smart grid (2013) IEEE Trans. Smart Grid, 4 (2), pp. 1007-1016; Cascella, D., Avitabile, G., Cannone, F., Coviello, G., A 2-GS/s 0.35m SiGe track-and-hold amplifier with 7-GHz analog bandwidth using a novel input buffer (2011) IEEE International Conference on Electronics, Circuits, and Systems, pp. 113-116; Aghajani, G.R., Shayanfar, H.A., Shayeghi, H., Demand side management in a smart micro-grid in the presence of renewable generation and demand response (2017) Energy, 126, pp. 622-637; Zhang, Y., Gatsis, N., Giannakis, G.B., Robust energy management for microgrids with high-penetration renewables (2013) IEEE Trans. Sust. Energ, 4 (4), pp. 944-953; Sun, Q., Ge, X., Liu, L., Xu, X., Zhang, Y., Niu, R., Review of smart grid comprehensive assessment systems (2011) Energy Procedia, 12, pp. 219-229; Carli, R., Dotoli, M., Energy scheduling of a smart home under nonlinear pricing (2014) IEEE Conf. Decis. Control; Guo, L., Wu, H.C., Zhang, H., Xia, T., Mehraeen, S., Robust optimization for home-load scheduling under price uncertainty in smart grids (2015) Int. Conf. Comp., Net. Commun; Digiesi, S., Mossa, G., Mummolo, G., Supply lead time uncertainty in a sustainable order quantity inventory model (2013) Management and Production Engineering Review, 4 (4), pp. 15-27; Safdarian, A., Fotuhi-Firuzabad, M., Lehtonen, M., A stochastic framework for short-term operation of a distribution company (2013) IEEE Trans. Power Syst, 28 (4), pp. 4712-4721; Ghasemi, A., Banejad, M., Rahimiyan, M., Integrated energy scheduling under uncertainty in a micro energy grid (2018) IET Gener. Transm. Distrib, 12 (12), pp. 2887-2896; Paridari, K., Parisio, A., Sandberg, H., Johansson, K.H., Robust scheduling of smart appliances in active apartments with user behavior uncertainty (2016) IEEE Trans. Autom. Sci. Eng, 13 (1), pp. 247-259; Pedrasa, M.A., Spooner, E.D., MacGill, I.F., Robust scheduling of residential distributed energy resources using a novel energy service decision-support tool (2011) ISGT, Anaheim; Zhang, C., Xu, Y., Dong, Z.Y., Ma, J., Robust operation of microgrids via two-stage coordinated energy storage and direct load control (2017) IEEE Trans. Power Systems, 32 (4), pp. 2858-2868; Wang, C., Zhou, Y., Jiao, B., Wang, D., Robust optimization for load scheduling of a smart home with photovoltaic system (2015) Energy Conversion and Management, 102, pp. 247-257; Xiang, Y., Liu, J., Liu, Y., Robust energy management of microgrid with uncertain renewable generation and load (2016) IEEE Trans. Smart Grid, 7 (2), pp. 1034-1043; Chen, Z., Wu, L., Fu, Y., Real-time price-based demand response management for residential appliances via stochastic optimization and robust optimization (2012) IEEE Trans. Smart Grid, 3 (4), pp. 1822-1831; Esther, B.P., Kumar, K.S., A survey on residential demand side management architecture, approaches, optimization models and methods (2016) Renew. Sustain. Energy. Rev, 59, pp. 342-351; Darivianakis, G., Georghiou, A., Smith, R.S., Lygeros, J., The power of diversity: Data-driven robust predictive control for energy-efficient buildings and districts IEEE Trans. Control Syst. Technol, in Press; Zhao, H.X., Magoul, F., A review on the prediction of building energy consumption (2012) Renewable and Sustainable Energy Reviews, 16 (6), pp. 3586-3592; Liu, X., Economic load dispatch constrained by wind power availability: A wait-and-see approach (2010) IEEE Trans. Smart Grid, 1 (3), pp. 347-355; Soyster, A.L., Convex programming with set-inclusive constraints and applications to inexact linear programming (1973) Operations Research, 21, pp. 1154-1157; Bertsimas, D., Brown, D.B., Caramanis, C., Theory and applications of robust optimization (2011) SIAM Review, 53 (3); Bertsimas, D., Sim, M., The price of robustness (2004) Operations Research, 52 (1), pp. 35-53}, document_type={Conference Paper}, source={Scopus}, }`

- Carli, R. & Dotoli, M. (2019) Decentralized control for residential energy management of a smart users’ microgrid with renewable energy exchange. IN IEEE/CAA Journal of Automatica Sinica, 6.641-656.

[Bibtex]`@ARTICLE{Carli2019641, author={Carli, R. and Dotoli, M.}, title={Decentralized control for residential energy management of a smart users’ microgrid with renewable energy exchange}, journal={IEEE/CAA Journal of Automatica Sinica}, year={2019}, volume={6}, number={3}, pages={641-656}, doi={10.1109/JAS.2019.1911462}, art_number={8707104}, note={cited By 43}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85065583902&doi=10.1109%2fJAS.2019.1911462&partnerID=40&md5=dfb517c8f1147f9d27b6d1082a326908}, abstract={This paper presents a decentralized control strategy for the scheduling of electrical energy activities of a microgrid composed of smart homes connected to a distributor and exchanging renewable energy produced by individually owned distributed energy resources. The scheduling problem is stated and solved with the aim of reducing the overall energy supply from the grid, by allowing users to exchange the surplus renewable energy and by optimally planning users x02BC controllable loads. We assume that each smart home can both buy x002F sell energy from x002F to the grid taking into account time-varying non-linear pricing signals. Simultaneously, smart homes cooperate and may buy x002F sell locally harvested renewable energy from x002F to other smart homes. The resulting optimization problem is formulated as a non-convex non-linear programming problem with a coupling of decision variables in the constraints. The proposed solution is based on a novel heuristic iterative decentralized scheme algorithm that suitably extends the Alternating Direction Method of Multipliers to a non-convex and decentralized setting. We discuss the conditions that guarantee the convergence of the presented algorithm. Finally, the application of the proposed technique to a case study under several scenarios shows its effectiveness. © 2014 Chinese Association of Automation.}, keywords={Automation; Decentralized control; Energy resources; Heuristic methods; Intelligent buildings; Microgrids; Nonlinear programming; Scheduling, Alternating direction method of multipliers; Decision variables; Distributed Energy Resources; Nonlinear programming problem; Optimization problems; Renewable energies; Residential energy; Scheduling problem, Iterative methods}, references={Gkatzikis, L., Salonidis, T., Hegde, N., Massoulie, L., Electricity markets meet the home through demand response (2012) Proc. IEEE Conference on Decision and Control, pp. 5846-5851; Tirado Herrero, S., Nicholls, L., Strengers, Y., Smart home technologies in everyday life: Do they address key energy challenges in households (2018) Current Opinion in Environmental Sustainability, 31, pp. 65-70; Kailas, A., Cecchi, V., Mukherjee, A., A survey of contemporary technologies for Smart Home energy management (2012) Handbook of Green Information and Communication Systems, pp. 35-56; Directive 2001/77/Ec (2001) Off. J. Eur., 6, pp. 33-40. , CEC; Carli, R., Dotoli, M., Pellegrino, R., A Hierarchical decision-making strategy for the energy management of smart cities (2017) IEEE Trans. Autom. Sci. Eng., 14 (2); Carli, R., Dotoli, M., Pellegrino, R., Ranieri, L., A decision making technique to optimize a buildings' stock energy efficiency (2017) IEEE Trans. Syst. Man, Cybern. Syst., 47 (5), pp. 794-807; Chakraborty, P., Baeyens, E., Khargonekar, P.P., Poolla, K., A cooperative game for the realized profit of an aggregation of renewable energy producers (2016) Proc. IEEE Conference on Decision and Control, pp. 5805-5812; Alizadeh, M., Chang, T.H., Scaglione, A., Grid integration of distributed renewables through coordinated demand response (2012) Proc. The IEEE Conference on Decision and Control, pp. 3666-3671; Liu, T., Tan, X., Sun, B., Wu, Y., Guan, X., Tsang, D.H.K., Energy management of cooperative microgrids with P2P energy sharing in distribution networks (2015) Proc. IEEE International Conference on Smart Grid Communications, pp. 410-415; Palensky, P., Dietrich, D., Demand Side Management: Demand Response, Intelligent Energy Systems, and Smart Loads (2011) IEEE Trans. Ind. Informatics, 7 (3), pp. 381-388; Lakshminarayana, S., Quek, T.Q.S., Poor, H.V., Cooperation and storage tradeoffs in power grids with renewable energy resources (2014) IEEE J. Sel. Areas Commun., 32 (7), pp. 1386-1397; Boyd, S., Parikh, N., Chu, E., Peleato, B., Eckstein, J., Distributed optimization and statistical learning via the alternating direction method of multipliers (2010) Found. Trends Mach. Learn., 3 (1), pp. 1-122; Barbato, A., Capone, A., Carello, G., Delfanti, M., Merlo, M., Zaminga, A., House energy demand optimization in single and multi-user scenarios Proc. 2011 IEEE International Conference on Smart Grid Communications, pp. 345-350; Deng, R., Yang, Z., Chen, J., Asr, N.R., Chow, M.Y., Residential energy consumption scheduling: A coupled-constraint game approach (2014) IEEE Trans. Smart Grid, 5 (3), pp. 1340-1350; Mohsenian-Rad, A.H., Wong, V.W.S., Jatskevich, J., Schober, R., Leon-Garcia, A., Autonomous demand-side management based on gametheoretic energy consumption scheduling for the future smart grid (2010) IEEE Trans. Smart Grid, 1 (3), pp. 320-331; Zhang, W., Xu, Y., Li, S., Zhou, M., Liu, W., Xu, Y., A distributed dynamic programming-based solution for load management in smart grids (2018) IEEE Systems Journal, 12 (1), pp. 402-413. , March; Xu, Y., Pan, F., Tong, L., Dynamic scheduling for charging electric vehicles: A priority rule (2016) IEEE Trans. Automat. Contr., 61 (12), pp. 4094-4099; Parise, F., Colombino, M., Grammatico, S., Lygeros, J., Mean field constrained charging policy for large populations of Plug-in Electric Vehicles (2014) Proc. IEEE Conference on Decision and Control, pp. 5101-5106; Le Floch, C., Belletti, F., Saxena, S., Bayen, A.M., Moura, S., Distributed optimal charging of electric vehicles for demand response and load shaping (2015) Proc. IEEE Conference on Decision and Control, pp. 6570-6576; Dong, Q., Yu, L., Song, W., Yang, J., Wu, Y., Qi, J., Fast distributed demand response algorithm in smart grid (2017) IEEE/CAA Journal of Automatica Sinica, 4 (2), pp. 280-296. , April; Atzeni, I., Ordonez, L., Scutari, G., Palomar, D., Fonollosa, J.R., Demand-side management via distributed energy generation and storage optimization (2013) IEEE Trans. Smart Grid, 4, pp. 866-876. , June; Adika, C.O., Wang, L., Autonomous appliance scheduling for household energy management (2014) IEEE Trans. Smart Grid, 5 (2), pp. 673-682; Cavraro, G., Carli, R., Zampieri, S., A distributed control algorithm for the minimization of the power generation cost in smart microgrid (2014) Proc. IEEE Conference on Decision and Control, pp. 5642-5647; Im, W., Wang, C., Liu, W., Liu, L., Kim, J., Distributed virtual inertia based control of multiple photovoltaic systems in autonomous microgrid (2017) IEEE/CAA Journal of Automatica Sinica, 4 (3), pp. 512-519; Wu, Y., Lau, V.K.N., Tsang, D.H.K., Qian, L.P., Meng, L., Optimal energy scheduling for residential smart grid with centralized renewable energy source (2014) IEEE Syst. J., 8 (2), pp. 562-576; Carli, R., Dotoli, M., A decentralized resource allocation approach for sharing renewable energy among interconnected smart homes (2015) Proc. IEEE Conference on Decision and Control, pp. 5903-5908; AlSkaif, T., Zapata, M.G., Bellalta, B., Nilsson, A., A distributed power sharing framework among households in microgrids: A repeated game approach (2017) Computing, 99 (1), pp. 23-37; Zhu, T., Huang, Z., Sharma, A., Su, J., Irwin, D., Mishra, A., Menasche, D., Shenoy, P., Sharing renewable energy in smart microgrids Proc. 2013 ACM/IEEE International Conference on Cyber-Physical Systems, pp. 219-228; Huang, Z., Zhu, T., Gu, Y., Irwin, D., Mishra, A., Shenoy, P., Minimizing electricity costs by sharing energy in sustainable microgrids (2014) Proc. ACM Conference on Embedded Systems for Energy-Efficient Buildings, pp. 120-129; Zhong, W., Huang, Z., Zhu, T., Gu, Y., Zhang, Q., Yi, P., Jiang, D., Xiao, S., IDES: Incentive-driven distributed energy sharing in sustainable microgrids (2014) Proc. International Green Computing Conference; Carli, R., Dotoli, M., A Decentralized Control Strategy for the Energy Management of Smart Homes with Renewable Energy Exchange (2018) Proc. IEEE Conference on Control Technology and Applications (CCTA), pp. 1662-1667; Fadlullah, Z.M., Fouda, M.M., Kato, N., Takeuchi, A., Iwasaki, N., Nozaki, Y., Toward intelligent machine-to-machine communications in smart grid (2011) IEEE Commun. Mag., 49 (4), pp. 60-65; Attivissimo, F., Di Nisio, A., Lanzolla, A.M.L., Paul, M., Feasibility of a photovoltaic-thermoelectric generator: Performance analysis and simulation results (2015) IEEE Trans. Instrum. Meas., 64 (5), pp. 1158-1169; Carli, R., Dotoli, M., Energy scheduling of a smart home under nonlinear pricing (2014) Proc. IEEE Conference on Decision and Control; Sanchez-Squella, A., Ortega, R., Grino, R., Malo, S., Dynamic energy router (2010) IEEE Control Syst. Mag., 30 (6), pp. 72-80; Styan, G.P.H., Hadamard products and multivariate statistical analysis (1973) Linear Algebra Appl., 6, pp. 217-240; Bemporad, A., Morari, M., Control of systems integrating logic, dynamics, and constraints (1999) Automatica, 35 (3), pp. 407-427; Boyd, S.S., Vandenberghe, L., Convex optimization (2004) Optim. Methods Softw., 25 (3), p. 487; Stubbs, R.A., Mehrotra, S., A branch-and-cut method for 0-1 mixed convex programming (1999) Math. Program. Ser. B, 86 (3), pp. 515-532; Fischetti, M., Glover, F., Lodi, A., The feasibility pump (2005) Math. Program., 104 (1), pp. 9-104; Marimon, R., Werner, J., (2017) The envelope theorem, Euler and Bellman equations, without differentiability, pp. 1-33; (1997) Convex Analysis., , R. Tyrrell Rockafellar; Wang, Y., Yin, W., Zeng, J., (2015) Global Convergence of ADMM in Nonconvex Nonsmooth Optimization; Diamond, S., Takapoui, R., Boyd, S., A general system for heuristic minimization of convex functions over non-convex sets Optimization Methods and Software, 33 (1), pp. 165-193; Vazirani, V.V., (2013) Approximation Algorithms, , Springer Science & Business Media; Deng, W., Lai, M.J., Peng, Z., Yin, W., Parallel multi-block ADMM with o(1 / k) convergence (2017) J. Sci. Comput., 71 (2), pp. 712-736; Bertsekas, D., (1999) Nonlinear Programming.; Hans, C.A., Braun, P., Raisch, J., Grune, L., Reincke-Collon, C., Hierarchical distributed model predictive control of interconnected microgrids (2018) IEEE Transactions on Sustainable Energy}, document_type={Article}, source={Scopus}, }`

- Dotoli, M. & Epicoco, N. (2019) Emerging issues in control, decision, and ICT Approaches for smart waste management IN 2019 6th International Conference on Control, Decision and Information Technologies, CoDIT 2019., 446-451.

[Bibtex]`@CONFERENCE{Dotoli2019446, author={Dotoli, M. and Epicoco, N.}, title={Emerging issues in control, decision, and ICT Approaches for smart waste management}, journal={2019 6th International Conference on Control, Decision and Information Technologies, CoDIT 2019}, year={2019}, pages={446-451}, doi={10.1109/CoDIT.2019.8820603}, art_number={8820603}, note={cited By 1}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85072826787&doi=10.1109%2fCoDIT.2019.8820603&partnerID=40&md5=09b23d63ad01f03c3b7e5a6d6065ce50}, abstract={Waste management is one of the major concerns of our times. This paper investigates the main issues in waste management, the classical practices and their limitations, and highlights the recent trends in the field to identify the foremost research areas whose advancement will lead to the achievement of smart waste management systems. © 2019 IEEE.}, keywords={Control engineering, In-control; Recent trends; Waste management systems, Waste management}, references={Powell, J.T., Chertow, M.R., Esty, D.C., Where is global waste management heading? An analysis of solid waste sector commitments from nationallydetermined contributions (2018) Waste Manag., 80, pp. 137-143; Dotoli, M., Epicoco, N., A vehicle routing technique for hazardous waste collection (2017) IFAC-PapersOnLine, 50 (1), pp. 9694-9699; Wilson, D.C., Velis, C.A., Rodic, L., Integrated sustainable waste management in developing countries (2013) Waste Resour. Manag., 166 (2), pp. 52-68; Moh, Y.C., Abd Manaf, L., Solid waste management transformation and future challenges of source separation and recycling practice in Malaysia (2017) Resour. Conserv. Recycl., 116, pp. 1-14; Shyamala, S.C., Sindhe, K., Muddy, V., Smart waste management system (2017) Int. J. Sci. Dev. Res., 1 (9), pp. 223-230; Aazam, M., St-Hilaire, M., Lung, C.H., Lambadaris, I., Cloud-based smart waste management for smart cities (2016) IEEE Int. Work. Computer Aided Modeling and Design of Communication Links and Networks, pp. 188-193; Pichtel, J., (2014) Waste Management Practices: Municipal, Hazardous, and Industrial, , CRC Press; Goel, S., (2017) Advances in Solid and Hazardous Waste Management, , Springer; Esmaeilian, B., Wang, B., Lewis, K., Duarte, F., Ratti, C., Behdad, S., The future of waste management in smart and sustainable cities: A review and concept paper (2018) Waste Manag., 81, pp. 177-195; Sengupta, D., Agrahari, S., (2017) Modelling Trends in Solid and Hazardous Waste Management, , Springer; Cavone, G., Dotoli, M., Epicoco, N., Seatzu, C., (2017) Intermodal Terminal Planning by Petri Nets and Data Envelopment Analysis, 69, pp. 9-22; Kolekar, K.A., Hazra, T., Chakrabarty, S.N., A review on prediction of municipal solid waste generation models (2016) Procedia Environ. Sci., 35, pp. 238-244; Gundupalli, S.P., Hait, S., Thakur, A., A review on automated sorting of source-separated municipal solid waste for recycling (2017) Waste Manag., 60, pp. 56-74; Kodali, R.K., Gorantla, V.S.K., Smart solid waste management (2017) 3rd Int. Conf. Applied and Theoretical Computing and Communication Technology, pp. 200-204; Laurent, A., Review of LCA studies of solid waste management systems (2014) Waste Manag., 34 (3), pp. 573-606; Karmperis, A.C., Aravossis, K., Tatsiopoulos, I.P., Sotirchos, A., Decision support models for solid waste management: Review and game-theoretic approaches (2013) Waste Manag., 33 (5), pp. 1290-1301; Melaré Souza De, A.V., González, S.M., Faceli, K., Casadei, V., Technologies and decision support systems to aid solid-waste management: A systematic review (2017) Waste Manag., 59, pp. 567-584; Shrivastava, P., Mishra, S., Katiyar, S.K., A review of solid waste management techniques using GIS and other technologies (2015) 2015 Int. Conf. Computational Intelligence and Communication Networks, pp. 1456-1459; Erses Yay, A.S., Application of life cycle assessment (LCA) for municipal solid waste management: A case study of sakarya (2015) J. Clean. Prod., 94, pp. 284-293; Coelho, L.M.G., Lange, L.C., Coelho, H.M.G., Multi-Criteria Decision Making to support waste management: A critical review of current practices and methods (2017) Waste Manag. Res., 35 (1), pp. 3-28; Coban, A., Ertis, I.F., Cavdaroglu, N.A., Municipal solid waste management via Multi-Criteria Decision Making methods: A case study in Istanbul, Turkey (2018) J. Clean. Prod., 180, pp. 159-167; Sarkis, J., Weinrach, J., Using data envelopment analysis to evaluate environmentally conscious waste treatment technology (2001) J. Clean. Prod., 9 (5), pp. 417-427; Soltani, A., Hewage, K., Reza, B., Sadiq, R., Multiple stakeholders in Multi-Criteria Decision-Making in the context of municipal solid waste management: A review (2015) Waste Manag., 35, pp. 318-328; Eiselt, H.A., Marianov, V., Location modeling for municipal solid waste facilities (2015) Comput. Oper. Res., 62, pp. 305-315; Habibi, F., Asadi, E., Sadjadi, S.J., Barzinpour, F., A multi-objective robust optimization model for site-selection and capacity allocation of municipal solid waste facilities: A case study in Tehran (2017) J. Clean. Prod., 166, pp. 816-834; McDougall, F.R., White, P.R., Franke, M., Hindle, P., (2007) Integrated Solid Waste Management: A Life Cycle Inventory, , Wiley Online Library; Yadav, V., Bhurjee, A.K., Karmakar, S., Dikshit, A.K., A facility location model for municipal solid waste management system under uncertain environment (2017) Sci. Total Environ., 603-604, pp. 760-771; Han, H., Cueto, E.P., Waste collection Vehicle Routing Problem: A literature review (2015) Promet-Traffic&Transportation, 27 (4), pp. 345-358; Dotoli, M., Epicoco, N., Falagario, M., Seatzu, C., Turchiano, B., Optimization of intermodal rail-road freight transport terminals (2014) 2014 IEEE Int. Conf. Robotics and Automation, pp. 1971-1976; Laner, D., Crest, M., Scharff, H., Morris, J.W.F., Barlaz, M.A., A review of approaches for the long-term management of municipal solid waste landfills (2012) Waste Manag., 32 (3), pp. 498-512; Turcott Cervantes, D.E., López Martínez, A., Cuartas Hernández, M., DeGarcía, C.A.L., Using indicators as A tool to evaluate municipal solid waste management: A critical review (2018) Waste Manag., 80, pp. 51-63; Ma, J., Hipel, K.W., Exploring social dimensions of municipal solid waste management around the globe-A systematic literature review (2016) Waste Manag., 56, pp. 3-12; Aghajani Mir, M., Application of TOPSIS and VIKOR improved versions in A multi criteria decision analysis to develop an optimized municipal solid waste management model (2016) J. Environ. Manage., 166, pp. 109-115; Meng, X., Wen, Z., Qian, Y., Multi-agent based simulation for household solid waste recycling behavior (2018) Resour. Conserv. Recycl., 128, pp. 535-545; Sun, W., An, C., Li, G., Lv, Y., Applications of inexact programming methods to waste management under uncertainty: Current status and future directions (2014) Environ. Syst. Res., 3 (1), pp. 1-15; Ghiani, G., Laganà, D., Manni, E., Musmanno, R., Vigo, D., Operations research in solid waste management: A survey of strategic and tactical issues (2014) Comput. Oper. Res., 44, pp. 22-32; Zacharof, A.I., Butler, A.P., Stochastic modelling of landfill processes incorporating waste heterogeneity and data uncertainty (2004) Waste Manag., 24 (3), pp. 241-250; Srivastava, A.K., Nema, A.K., Fuzzy parametric programming model for multi-objective integrated solid waste management under uncertainty (2012) Expert Syst. Appl., 39 (5), pp. 4657-4678; Dotoli, M., Epicoco, N., Falagario, M., Sciancalepore, F., A cross-efficiency fuzzy data envelopment analysis technique for performance evaluation of decision making units under uncertainty (2015) Comput. Ind. Eng., 79, pp. 103-114; Cavone, G., Dotoli, M., Epicoco, N., Morelli, D., Seatzu, C., A game-theoretical design technique for multi-stage supply chains under uncertainty (2018) 14th IEEE Int. Conf. Automation Science and Engineering, pp. 528-533; Anagnostopoulos, T., Challenges and opportunities of waste management in IoT-enabled smart cities: A survey (2017) IEEE Trans. Sustain. Comput., 2 (3), pp. 275-289; Khan, D., Samadder, S.R., Municipal solid waste management using Geographical Information System aided methods: A mini review (2014) Waste Manag. Res., 32 (11), pp. 1049-1062; Abdoli, S., RFID application in municipal solid waste management system (2009) Int. J. Environ. Res., 3 (3), pp. 447-454; Kannan, S., Kumar, S., Balakrishnan, R.R., Automatic garbage separation robot using image processing technique (2016) Int. J. Sci. Res. Publ., 6 (4), pp. 326-328; Bonino, D., Alizo, M.T.D., Pastrone, C., Spirito, M., WasteApp: Smarter waste recycling for smart citizens (2016) 2016 Int. Multidisciplinary Conf. Computer and Energy Science, pp. 1-6}, document_type={Conference Paper}, source={Scopus}, }`

- Cavone, G., Blenkers, L., Van Den Boom, T., Dotoli, M., Seatzu, C. & De Schutter, B. (2019) Railway disruption: A bi-level rescheduling algorithm IN 2019 6th International Conference on Control, Decision and Information Technologies, CoDIT 2019., 54-59.

[Bibtex]`@CONFERENCE{Cavone201954, author={Cavone, G. and Blenkers, L. and Van Den Boom, T. and Dotoli, M. and Seatzu, C. and De Schutter, B.}, title={Railway disruption: A bi-level rescheduling algorithm}, journal={2019 6th International Conference on Control, Decision and Information Technologies, CoDIT 2019}, year={2019}, pages={54-59}, doi={10.1109/CoDIT.2019.8820380}, art_number={8820380}, note={cited By 5}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85072825857&doi=10.1109%2fCoDIT.2019.8820380&partnerID=40&md5=1402c9b7c47196a37e50ebaba061fdef}, abstract={The real-time rescheduling of railway traffic in case of unexpected events is a challenging task. This is mainly due to the complexity of the railway service, which has to ensure safety, punctuality, and efficiency to customers by respecting timetable, framework, and resources constraints. Most of the available researches focus on short delays (i.e., disturbances). Approaches typically rely on simplified macroscopic models for large-scale systems or detailed microscopic models for one or a few lines, due to the long computation time required for solving the rescheduling problem. Only a small number of works consider rescheduling in case of long delays (i.e., disruptions) and all of them are also based on either a macroscopic or a microscopic model. This research focuses on disruptions and aims at filling the gap between macroscopic and microscopic modelling by proposing an innovative bi-level rescheduling algorithm based on a mesoscopic Mixed Integer Linear Programming (MILP) model. The technique allows obtaining a feasible rescheduled timetable in a short computation time respecting not only timetable and safety constraints (typical of macroscopic models) but also capacity and ordering constraints for the disrupted stations (typical of microscopic models). The bi-level algorithm first solves the macroscopic MILP rescheduling problem and then, considering the cancellation and non-admissible platform assignments results, it solves a mesoscopic MILP rescheduling problem. This allows to significantly reduce the search space and consequently the computation time. The method is tested for the rescheduling of the Dutch railway traffic in case of a full blockade between two consecutive stations. © 2019 IEEE.}, keywords={Large scale systems; Railroad transportation; Railroads; Scheduling; Superconducting materials, Macroscopic and microscopic; Microscopic modeling; Microscopic models; Mixed integer linear programming model; Ordering constraints; Platform assignments; Real-time rescheduling; Rescheduling problem, Integer programming}, references={Dotoli, M., Epicoco, N., Falagario, M., Turchiano, B., Cavone, G., Convertini, A., A decision support system for real-time rescheduling of railways (2014) 2014 European Control Conference, pp. 696-701; Cavone, G., Dotoli, M., Epicoco, N., Seatzu, C., Intermodal terminal planning by petri nets and data envelopment analysis (2017) Control Eng. Pract., 69, pp. 9-22; Kersbergen, B., Van Den Boom, T., De Schutter, B., Distributed model predictive control for railway traffic management (2016) Transp. Res. Part C Emerg. Technol., 68, pp. 462-489. , Jul; Li, X., Shou, B., Ralescu, D., Train rescheduling with stochastic recovery time: A new track-backup approach (2014) IEEE Trans. Syst. Man, Cybern. Syst., 44 (9), pp. 1216-1233. , Sep; Cacchiani, V., An overview of recovery models and algorithms for real-time railway rescheduling (2014) Transp. Res. Part B: Methodological, 63, pp. 15-37. , Pergamon. May; Dollevoet, T., Huisman, D., Kroon, L.G., Veelenturf, L.P., Wagenaar, J.C., Application of an iterative framework for real-time railway rescheduling (2017) Comput. Oper. Res., 78, pp. 203-217. , Feb; Fang, W., Yang, S., Yao, X., A survey on problem models and solution approaches to rescheduling in railway networks (2015) IEEE Trans. On Intell. Transp. Sys., 16 (6), pp. 2997-3016. , Dec; Jacobs, J., Reducing delays by means of computer-aided 'on-the-spot' rescheduling (2004) Adv. Transp., 15, pp. 603-612. , May; Pellegrini, P., Marliere, G., Pesenti, R., Rodriguez, J., Recifemilp: An effective milp-based heuristic for the real-time railway traffic management problem (2015) IEEE Trans. Intell. Transp. Syst., 16 (5), pp. 2609-2619. , Oct; Pellegrini, P., Marlière, G., Rodriguez, J., Real time railway traffic management modeling track-circuits (2012) OpenAccess Ser. Informatics, 25, pp. 23-34. , Jan; Törnquist, J., Persson, J.A., N-tracked railway traffic re-scheduling during disturbances (2007) Transp. Res. Part B Methodol., 41 (3), pp. 342-362. , Mar; Törnquist, J., Computer-based decision support for railway traffic scheduling and dispatching: A review of models and algorithms (2006) Algorithmic MeThods Model. Optim. Rail-ways, 2, p. 23p; Narayanaswami, S., Rangaraj, N., Modelling disruptions and resolving conflicts optimally in a railway schedule (2013) Comput. Ind. Eng., 64 (1), pp. 469-481. , Jan; Ghaemi, N., Cats, O., Goverde, R.M.P., A microscopic model for optimal train short-turnings during complete blockages (2017) Transp. Res. Part B Methodol., 105, pp. 423-437. , Nov; Blenkers, L.L., Van Den Boom, J.T.J., Kersbergen, B., An exploratory study on railway disruption management using switching max-plus linear models (2017) Rail Lille-7th International Conference on Railway Operations Modelling and Analysis, pp. 334-352}, document_type={Conference Paper}, source={Scopus}, }`

- Carli, R. & Dotoli, M. (2019) Distributed Control for Waterfilling of Networked Control Systems with Coupling Constraints IN Proceedings of the IEEE Conference on Decision and Control., 3710-3715.

[Bibtex]`@CONFERENCE{Carli20193710, author={Carli, R. and Dotoli, M.}, title={Distributed Control for Waterfilling of Networked Control Systems with Coupling Constraints}, journal={Proceedings of the IEEE Conference on Decision and Control}, year={2019}, volume={2018-December}, pages={3710-3715}, doi={10.1109/CDC.2018.8619425}, art_number={8619425}, note={cited By 3}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85062189943&doi=10.1109%2fCDC.2018.8619425&partnerID=40&md5=3dfd050026baf187539419a0a975db57}, abstract={In this paper we present a distributed control approach for the multi-user multi-constrained waterfilling. This a specific category of distributed optimization for Networked Control Systems (NCSs), where agents aim at optimizing a non-separable global objective function while satisfying both local constraints and coupling constraints. Differently from the existing literature, in the considered setting we adopt a fully distributed mechanism where communication is allowed between neighbors only. First, we formulate a general multi-user waterfilling-structured optimization problem including coupling constraints, which may represent many engineering distributed control problems. Successively, we define a low-complexity iterative distributed algorithm based on duality, consensus and fixed point mapping theory. Finally, applying the technique to a simulated case referring to the electric vehicles optimal charging problem, we show its effectiveness. © 2018 IEEE.}, keywords={Computational complexity; Distributed parameter control systems; Iterative methods, Coupling constraints; Distributed control; Distributed control problems; Distributed optimization; Global objective functions; Local constraints; Networked Control Systems (NCSs); Structured optimization problem, Networked control systems}, references={Gupta, R.A., Chow, M.Y., Networked control systems: Overview and research trends (2010) IEEE Trans. Ind. Electron, 57 (7), pp. 2527-2535; Gallager, R.G., (1968) Information Theory and Reliable Communication, , New York: Wiley; Stavrou, P.A., Charalambous, T., Charalambous, C.D., Filtering with fidelity for time-varying Gauss-Markov processes (2016) Proc. IEEE CDC, pp. 5465-5470. , Dec; Fang, S., Ishii, H., Chen, J., Trade-offs in information-limited feedback systems: MIMO Bode-type integrals and power allocation (2015) Proc. IEEE CDC, pp. 6178-6183. , Dec; Tzortzis, C.D., Charalambous, T., Infinite horizon average cost dynamic programming subject to ambiguity on conditional distribution (2015) Proc. IEEE CDC, pp. 7171-7176; Zhao, H., Yan, A., Zhang, C., Wang, P., An optimizing method based on water-filling for case attribute weight (2012) Proc. 24th Chin. Control Decis. Conf, pp. 3455-3458. , China; Gkatzikis, L., Salonidis, T., Hegde, N., Massoulié, L., Electricity markets meet the home through demand response (2012) Proc. IEEE CDC, pp. 5846-5851. , Dec; Palomar, D.P., Fonollosa, J.R., Practical algorithms for a family of waterfilling solutions (2005) IEEE Trans. Signal Proces.s, 53 (2), pp. 686-695; Scutari, G., Palomar, D.P., Barbarossa, S., Optimal linear precoding strategies for wideband non-cooperative systems based on game theory-part II: Algorithms (2008) IEEE Trans. Signal Process, 56 (3), pp. 1250-1267. , March; Carli, R., Dotoli, M., A distributed control algorithm for waterfilling of networked control systems via consensus (2017) IEEE Control Systems Letters, 1 (2), pp. 334-339. , Oct; Gan, L., Topcu, U., Low, S., Optimal decentralized protocol for electric vehicle charging' (2013) IEEE Trans. Power Syst., 28(2), pp. 940-951; He, P., Li, M., Zhao, L., Venkatesh, B., Li, H., Water-filling exact solutions for load balancing of smart power grid systems (2018) IEEE Trans. Smart Grid, 9 (2), pp. 1397-1407. , March; Boyd, S., Vandenberghe, L., (2004) Convex Optimization, , Cambridge University Press, UK; Carli, R., Dotoli, M., A decentralized resource allocation approach for sharing renewable energy among interconnected smart homes (2015) IEEE Int. Conf. Dec. Contr, , Dec. 15-18; Grammatico, S., Aggregative control of large populations of noncooperative agents (2016) Proc. IEEE Int. Conf. Dec. Contr, pp. 4445-4450; Chong, E.K., Zak, S.H., (2013) An Introduction to Optimization, 76. , John Wiley &Sons; Rockafellar, R.T., Wets, R.J.B., (1998) Variational Analysis, , Springer; Berinde, V., (2007) Iterative Approximation of Fixed Points, , Springer; Palomar, D.P., Convex primal decomposition for multicarrier linear MIMO transceivers (2005) IEEE IEEE Trans. Signal Process, 53 (12), pp. 4661-4674; Falsone, A., Margellos, K., Garatti, S., Prandini, M., Distributed constrained convex optimization and consensus via dual decomposition and proximal minimization (2016) Proc. IEEE CDC, pp. 1889-1894; Carli, R., Dotoli, M., A distributed control algorithm for optimal charging of electric vehicle fleets with congestion management (2018) IFAC Symposium on Control in Transportation Systems (CTS) 2018, pp. 373-378. , IFAC PapersOnLine 51-9; Ren, W., Beard, R.W., Overview of consensus algorithms in cooperative control (2008) Distributed Consensus in Multi-vehicle Cooperative Control: Theory and Applications, pp. 3-22}, document_type={Conference Paper}, source={Scopus}, }`

- Hosseini, S. M., Carli, R. & Dotoli, M. (2019) Model Predictive Control for Real-Time Residential Energy Scheduling under Uncertainties IN Proceedings – 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018., 1386-1391.

[Bibtex]`@CONFERENCE{Hosseini20191386, author={Hosseini, S.M. and Carli, R. and Dotoli, M.}, title={Model Predictive Control for Real-Time Residential Energy Scheduling under Uncertainties}, journal={Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018}, year={2019}, pages={1386-1391}, doi={10.1109/SMC.2018.00242}, art_number={8616238}, note={cited By 25}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85062230383&doi=10.1109%2fSMC.2018.00242&partnerID=40&md5=2f024e90b4a067ff3c318f9e0a035f00}, abstract={This paper proposes a real-time strategy based on Model Predictive Control (MPC) for the energy scheduling of a grid-connected smart residential user equipped with deferrable and non-deferrable electrical appliances, a renewable energy source (RES), and an electrical energy storage system (EESS). The proposed control scheme relies on an iterative finite horizon on-line optimization, implementing a quadratic cost function to minimize the electricity bill of the user's load demand and to limit the peak-to-average ratio (PAR) of the energy consumption profile whilst considering operational constraints. At each time step, the optimization problem is solved providing the cost-optimal energy consumption profile for the user's deferrable loads and the optimal charging/discharging profile for the EESS, taking into account forecast uncertainties by using the most updated predicted values of local RES generation and non-deferrable loads consumption. The performance and effectiveness of the proposed framework are evaluated for a case study where the dynamics of the considered residential energy system is simulated under uncertainties both in the forecast of the RES generation and the non-deferrable loads energy consumption. © 2018 IEEE.}, author_keywords={energy scheduling; model predictive control (MPC); residential energy management; uncertainties}, keywords={Cost functions; Cybernetics; Electric energy storage; Energy utilization; Housing; Predictive control systems; Renewable energy resources; Scheduling, Electrical energy storage systems; Operational constraints; Quadratic cost functions; Renewable energy source; Residential energy; Residential energy systems; Scheduling under uncertainty; uncertainties, Model predictive control}, references={Li, Q., Xu, Z., Oka, K., Yang, L., Recent advancements on the development of microgrids (2014) J. Mod. Power Syst. Cle., 2 (3), pp. 206-211; Hou, L., Wang, C., Market-based mechanisms for smart grid management: Necessity, applications and opportunities (2017) IEEE. Int. Conf. Systems, Man, and Cybernetics, , October; Carli, R., Dotoli, M., Cooperative distributed control for the energy scheduling of smart homes with shared energy storage and renewable energy source (2017) IFAC World Congress., 50 (1), pp. 8867-8872; Carli, R., Dotoli, M., Energy scheduling of a smart home under nonlinear pricing (2014) IEEE Conf Decis Control; Ma, J., Chen, H.H., Song, L., Li, Y., Residential load scheduling in smart grid: A cost efficiency perspective (2016) IEEE. Trans. Smart Grid, 7 (2), pp. 771-784; Carli, R., Dotoli, M., A decentralized resource allocation approach for sharing renewable energy among interconnected smart homes (2015) IEEE. Conf Decis Control; Rahmani-Andebili, M., Scheduling deferrable appliances and energy resources of a smart home applying multi-Time scale stochastic model predictive control (2017) Sustain. Cities. Soc., 32, pp. 338-347; Rahmani-Andebili, M., Shen, H., Cooperative distributed energy scheduling for smart homes applying stochastic model predictive control (2017) IEEE Int. Conf. Communications, , May; Parisio, A., Wiezorek, C., Kyntaja, T., Elo, J., Johansson, K.H., An mpc-based energy management system for multiple residential microgrids (2015) IEEE CASE; Samadi, P., Mohsenian-Rad, H., Wong, V.W.S., Schober, R., Tackling the load uncertainty challenges for energy consumption scheduling in smart grid (2013) IEEE. Trans. Smart Grid, 4 (2), pp. 1007-1016; Mohsenian-Rad, A.H., Wong, V.W.S., Jatskevich, J., Schober, R., Optimal and autonomous incentive-based energy consumption scheduling algorithm for smart grid (2010) Innovative Smart Grid Technologies, , January; Barbato, A., Capone, A., Optimization models and methods for demandside management of residential users: A survey (2014) Energies, 7 (9), pp. 5787-5824; Esther, B.P., Kumar, K.S., A survey on residential demand side management architecture, approaches, optimization models and methods (2016) Renew Sust Energ Rev, 59, pp. 342-351; Liu, X., Economic load dispatch constrained by wind power availablity: A wait-And-see approach (2010) IEEE Trans. Smart Grid, 1 (3), pp. 347-355; Zack, J., (2003) Overview of Wind Energy Generation Forecasting., , Draft Report for NY State Energy Research and Development Authority and for NY ISO, True Wind Solutions LLC, NY, USA; Wu, Y., Lau, V.K.N., Tsang, D.H.K., Qian, L.P., Meng, L., Optimal energy scheduling for residential smart grid with centralized renewable energy source (2014) IEEE Systems Journal, 8 (2), pp. 562-576; Zhao, H.X., Magoulès, F., A review on the prediction of building energy consumption (2012) Renewable and Sustainable Energy Reviews, 16 (6), pp. 3586-3592; Camacho, E.F., Alba, C.B., (2013) Model predictive Control., , Springer Science & Business Media; Cococcioni, M., D'Andrea, E., Lazzerini, B., 24-hour-Ahead forecasting of Energy production in solar PV Systems (2011) Intelligent Systems Design and Applications (ISDA). 2011 11th InterNational Conference on, pp. 1276-1281. , November. IEEE; Schlegel, S., Korn, N., Scheuermann, G., On the interpolation of data with normally distributed uncertainty for visualization (2012) IEEE Trans. Vis. Comput. Gr., 18 (12), pp. 2305-2314}, document_type={Conference Paper}, source={Scopus}, }`

- Othman, S. B., Hammadi, S., Zgaya, H., Renard, J. -M. & Dotoli, M. (2019) Dynamic schedule execution to improve adult emergency department performance in real-time IN 33rd Annual European Simulation and Modelling Conference 2019, ESM 2019., 272-278.

[Bibtex]`@CONFERENCE{Othman2019272, author={Othman, S.B. and Hammadi, S. and Zgaya, H. and Renard, J.-M. and Dotoli, M.}, title={Dynamic schedule execution to improve adult emergency department performance in real-time}, journal={33rd Annual European Simulation and Modelling Conference 2019, ESM 2019}, year={2019}, pages={272-278}, note={cited By 0}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85076226919&partnerID=40&md5=f1aa7d4632e0dfd85e1f406b8809d153}, abstract={An Emergency Department (ED) is a very complex system involving heterogeneous patients and several kinds of resources that evolve within a sophisticated process. The management methodology should be chosen in a more effective and targeted way so as to meet the increasing patients' requirements. Our objective is to find out fast solutions for unscheduled arrivals, dynamic competing priorities and heterogeneous patient care needs. The primary objective of this article is to provide ED managers with internal cost-effective solutions and perceptions in order to reduce overcrowding phenomenon impacts and enhance ED performance. Simulation results show that our scheduling method can significantly reduce the total response time of patients. Copyright © 2019 EUROSIS-ETI.}, author_keywords={Emergency Department; Overcrowding; Response time; Scheduling}, keywords={Cost effectiveness; Modal analysis; Response time (computer systems); Scheduling, Dynamic schedule; Emergency departments; Fast solutions; Internal costs; Management methodologies; Overcrowding; Primary objective; Scheduling methods, Emergency rooms}, references={Abo-Hamad, W., Ansha, A., Simulation-based framework to improve patient experience in an emergency department (2013) European Journal of Operational Research, 224, pp. 154-166; Azadeh, A., Hosseinabadi Faraham, M., Torabzadeh, S., Baghersad, M., Scheduling pnontized patients in emergency department laboratories (2014) Computer Methods and Program in Biomedicine, 117, pp. 61-70; Brailsford, S.C., Harper, P.R., Patel, B., Pitt, M., An analysis of the academic literature on simulation and modelling in health care (2009) Journal of Simulation, 3 (3), pp. 130-140; Cameron, P.A., Schull, M.J., Cooke, M.W., A framework for measuring quality in the emergency department (2011) Emergency Medicine Journal, 28 (9), pp. 735-740; Demeester, P., Souffriau, W., Causmaecker, P.D., Berghe, G.V., A hybnd tabu search algorithm for automatically assigning patients to beds (2010) Journal of Artif Intell. Med., 48, pp. 61-70; Harrison, J., Ferguson, E., The cnsis in United States hospital emergency services (2011) International Journal of Health Care Quality Assurance, 24 (6), pp. 471-483; Hoot, N., Aronsky, A., Systematic review of emergency department crowding: Causes, effects, and solutions (2008) Annals of Emergency Medicine, 52 (2), pp. 126-136; Hupert, N., Hollmgsworth, R., Xiong, W., Is overtnage associated with increased mortality? Insights from a simulation model of mass casualty trauma care Disaster (2007) Med Public Health Prep, 1 (1), pp. S14-S24; Luscombe, R., Kozan, E., Dynamic resource allocation to improve emergency department efficiency in real time (2016) European Journal of Operational Research, 255, pp. 593-603; Mm, D., Yih, Y.W., An elective surgery scheduling problem considering patient pnonty (2010) Comput. Oper. Res., 37, pp. 1091-1099; Niska, R., Bhuiya, F., Xu, J., National hospital ambulatory medical care survey: 2007 emergency department summary (2010) National Center for Health Statistics, p. 26; Salway, R.J., Valenzuela, R., Shoenberger, J.M., Mallon, W.K., Viccelho, Emergency department (ED) overcrowding: Evidence-based answers to frequently asked questions (2017) Rev. Med. Clin. Condes., 28 (2), pp. 213-219}, document_type={Conference Paper}, source={Scopus}, }`

- Carli, R., Dotoli, M. & Pellegrino, R. (2019) A multi-period approach for the optimal energy retrofit planning of street lighting systems. IN Applied Sciences (Switzerland), 9..

[Bibtex]`@ARTICLE{Carli2019, author={Carli, R. and Dotoli, M. and Pellegrino, R.}, title={A multi-period approach for the optimal energy retrofit planning of street lighting systems}, journal={Applied Sciences (Switzerland)}, year={2019}, volume={9}, number={5}, doi={10.3390/app9051025}, art_number={1025}, note={cited By 5}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85063660288&doi=10.3390%2fapp9051025&partnerID=40&md5=80ca57851d94e4757320cee529983b5f}, abstract={Investing in the optimal measures for improving the energy efficiency of urban street lighting systems has become strategic for the economic, technological and social development of cities. The decision-making process for the selection of the optimal set of interventions is not so straightforward. Several criticalities-such as difficulties getting access to credit for companies involved in street lighting systems refurbishment, budget constraints of municipalities, and unawareness of the actual energy and economic performance after a retrofitting intervention-require a decision-making approach that supports the city energy manager in selecting the optimal street lighting energy efficiency retrofitting solution while looking not only based on the available budget, but also based on the future savings in energy expenditures. In this context, the purpose of our research is to develop an effective decision-making model supporting the optimal multi-period planning of the street lighting energy efficiency retrofitting, which proves to be more effective and beneficial than the classical single-period approach and has never before been applied to the considered public lighting system context. The proposed methodology is applied to a real street lighting system in the city of Bari, Italy, showing the energy savings and financial benefit obtained through the proposed method. Numerical experiments are used to investigate and quantify the effects of using a multi-period planning approach instead of a single-period approach. © 2019 by the authors.}, author_keywords={Energy efficiency management; Multi-period planning; Optimization; Street lighting}, references={Beccali, M., Bonomolo, M., Ciulla, G., Galatioto, A., Brano, V.L., Improvement of energy efficiency and quality of street lighting in South Italy as an action of Sustainable Energy Action Plans The case study of Comiso (RG) (2015) Energy, 92, pp. 394-408; Pizzuti, S., Annunziato, M., Moretti, F., Smart street lighting management (2013) Energy Effic, 6, pp. 607-616; Richards, M., Carter, D., Good lighting with less energy (2009) Light. Res. Technol, 41, p. 285; Annunziato, M., Honorato Consonni, C., De Lia, F., Fumagalli, S., Giuliani, G., Gozo, N., Scognamiglio, A., (2012) LINEE GUIDA: I Fondamentali per una Gestione Efficiente Degli Impianti di Pubblica Illuminazione, , ENEA: Roma, Italy; (2013), http://www.covenantofmayors.eu/index_en.html, (accessed on 21 December 2018); Popa, M., Cepişca, C., Energy consumption saving solutions based on intelligent street lighting control system (2011) UPB Sci. Bull. Ser. C, 73, pp. 297-308; Raciti, A., Rizzo, S.A., Susinni, G., Parametric PSpice Circuit of Energy Saving Lamp Emulating Current Waveform (2019) Appl. Sci, 9, p. 152; Carli, R., Dotoli, M., Pellegrino, R., A decision-making tool for energy efficiency optimization of street lighting (2018) Comput. Oper. Res, 96, pp. 223-235; Cristea, M., Tîrnovan, R.A., Cristea, C., Pica, C.S., Fagaras an, C., A multi-criteria decision making approach for public lighting system selection (2018) MATEC Web Conf, 184; Beccali, M., Bonomolo, M., Leccese, F., Lista, D., Salvadori, G., On the impact of safety requirements, energy prices and investment costs in street lighting refurbishment design (2018) Energy, 165, pp. 739-759; Stuart, E., Larsena, P.H., Goldmana, C.A., Gilliganc, D., A method to estimate the size and remaining market potential of the U.S (2014) ESCO (energy service company) industry. Energy, 77, pp. 362-371; Suhonena Okkonenb, L., The Energy Services Company (ESCo) as business model for heat entrepreneurship-A case study of North Karelia, Finland (2013) Energy Policy, 61, pp. 783-787; Carbonara, N., Pellegrino, R., Public-private partnerships for energy efficiency projects: A win-win model to choose the energy performance contracting structure (2018) J. Clean. Prod, 170, pp. 1064-1075; Yao, Q., Wang, H., Uttley, J., Zhuang, X., Illuminance Reconstruction of Road Lighting in Urban Areas for Efficient and Healthy Lighting Performance Evaluation (2018) Appl. Sci, 8, p. 1646; Tan, B., Yavuz, Y., Otay, E.N., Çamlibel, E., Optimal selection of energy efficiency measures for energy sustainability of existing buildings (2016) Comput. Oper. Res, 66, pp. 258-271; Lauro, F., Longobardi, L., Panzieri, S., An adaptive distributed predictive control strategy for temperature regulation in a multizone office building (2014) Proceedings of the 2014 IEEE International Workshop on Intelligent Energy Systems (IWIES), pp. 32-37. , San Diego, CA, USA, 8 October; Pellegrino, R., Costantino, N., Giustolisi, O., Flexible investment planning for water distribution networks (2018) J. Hydroinform, 20, pp. 18-33; Digiesi, S., Facchini, F., Mossa, G., Mummolo, G., Verriello, R., A cyber-based DSS for a low carbon integrated waste management system in a smart city (2015) IFAC-PapersOnLine, 48, pp. 2356-2361; Dickinson, M.W., Thornton, A.C., Graves, S., Technology portfolio management: Optimizing interdependent projects over multiple time periods (2001) IEEE Trans. Eng. Manag, 48, pp. 518-527; Leccese, F., Salvadori, G., Rocca, M., Critical analysis of the energy performance indicators for road lighting systems in historical towns of central Italy (2017) Energy, 138, pp. 616-628; Martello, S., (1990) Knapsack Problems: Algorithms and Computer Implementations, , Wiley-Interscience Series in Discrete Mathematics and Optimization;Wiley: Hoboken, NJ, USA; Wang, B., Xia, X., Optimal maintenance planning for building energy efficiency retrofitting from optimization and control system perspectives (2015) Energy Build, 96, pp. 299-308; Carli, R., Dotoli, M., Pellegrino, R., A Hierarchical Decision Making Strategy for the Energy Management of Smart Cities (2017) IEEE Trans. Autom. Sci. Eng, 14, pp. 505-523; Ranieri, L., Mossa, G., Pellegrino, R., Digiesi, S., Energy Recovery from the Organic Fraction of Municipal Solid Waste: A Real Options-Based Facility Assessment (2018) Sustainability, 10, p. 368; Bruno, S., D'Aloia, M., De Benedictis, M., Lamonaca, S., La Scala, M., Rotondo, G., Stecchi, U., (2011) Studio di Fattibilità per la Integrazione di un Modello di Pubblica Illuminazione ad Alta Efficienza in un Power Park Urbano (Quartiere Eco-Sostenibile): Analisi di un Caso Pilota, , http://www.enea.it/it/Ricerca_sviluppo/documenti/ricerca-di-sistema-elettrico/smart-city/rds-328.pdf, accessed on 21 December 2018). (In Italian; (2015) EN 13201e2. Light and Lighting. Road Lighting-Part 2: Performance Requirements, , European Committee for Standardization: Brussels, Belgium; (2015) EN 13201e2. Light and Lighting. Road Lighting-Part 3: Calculation of Performance, , European Committee for Standardization: Brussels, Belgium; (2015) EN 13201e4. Light and Lighting. Road Lighting-Part 4: Methods of Measuring Lighting Performance, , European Committee for Standardization: Brussels, Belgium; Rea, M.S., (2000) The IESNA Lighting Handbook, , Illuminating Engineering Society of North America: New York, NY, USA; Lagorse, J., Paire, D., Miraoui, A., Sizing optimization of a stand-alone street lighting system powered by a hybrid system using fuel cell, PV and battery (2009) Renew. Energy, 34, pp. 683-691; http://www.lighting.philips.com/main/systems/systemareas/roads-and-streets, (accessed on 21 December 2018); http://www.regione.puglia.it/elencoprezzi-2017, (accessed on 21 December 2018). (In Italian); http://appsso.eurostat.ec.europa.eu/nui/submitViewTableAction.do, (accessed on 21 December 2018); IBM ILOG CPLEX Optimization Studio Getting Started with CPLEX for MATLAB, , https://www.ibm.com/support/knowledgecenter/en/SSSA5P_12.6.2/ilog.odms.cplex.help/CPLEX/MATLAB/topics/gs.html, (accessed on 21 December 2018); Brealey, R., Myers, S., (2000) Principles of Corporate Finance, , McGraw-Hill: Irwin, PA, USA; Jain, P.K., (1999) Theory and Problems in Financial Management, , Tata McGraw-Hill Education: New Delhi, India}, document_type={Article}, source={Scopus}, }`

- Dotoli, M., Fay, A., Miśkowicz, M. & Seatzu, C. (2019) An overview of current technologies and emerging trends in factory automation. IN International Journal of Production Research, 57.5047-5067.

[Bibtex]`@ARTICLE{Dotoli20195047, author={Dotoli, M. and Fay, A. and Miśkowicz, M. and Seatzu, C.}, title={An overview of current technologies and emerging trends in factory automation}, journal={International Journal of Production Research}, year={2019}, volume={57}, number={15-16}, pages={5047-5067}, doi={10.1080/00207543.2018.1510558}, note={cited By 28}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85052118672&doi=10.1080%2f00207543.2018.1510558&partnerID=40&md5=ea5b29c60f57c7cfeb39ace72401c504}, abstract={In this paper we provide an overview of recent theoretical approaches and technologies that respond to the fundamental challenges of modern factory automation. We classify these major methods and technologies into several groups and, for seven of them - namely: vertical integration of factory automation systems; distributed and decentralised control, smart sensors and actuators in factories; networked control systems and wireless sensors and actuators; autonomy and self-organisation of factories; advanced sensing for factory automation; semantic models of factories; engineering methods of factory automation systems - we report recent research contributions and formulate open technical problems in the domain of modern factory automation. © 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group.}, author_keywords={advanced sensing; autonomous systems; decentralised control; distributed control; engineering methods; factory automation; manufacturing systems; networked control systems; self-organisation; semantic models; smart sensors and actuators; vertical integration; wireless sensor networks}, keywords={Actuators; Decentralized control; Distributed parameter control systems; Factory automation; Manufacture; Semantics; Smart sensors; Wireless sensor networks, advanced sensing; Autonomous systems; Decentralised control; Distributed control; Engineering methods; Self organisation; Semantic Model; Sensors and actuators; Vertical integration, Networked control systems}, references={Abrishambaf, R., Hashemipour, M., Bal, M., Structural Modeling of Industrial Wireless Sensor and Actuator Networks for Reconfigurable Mechatronic Systems (2013) The International Journal of Advanced Manufacturing Technology, 64 (5-8), pp. 793-811; Åkerberg, J., Gidlund, M., Bjorkman, M., Future Research Challenges in Wireless Sensor and Actuator Networks Targeting Industrial Automation (2011) 2011 9th IEEE International Conference on Industrial Informatics, Caparica, Lisbon, pp. 410-415. , https://doi.org/10.1109/INDIN.2011.6034912; Akyildiz, I., Su, W., Sankarasubramaniam, Y., Cayirci, E., A Survey on Sensor Networks (2002) IEEE Communication Magazine, 40 (8), pp. 102-114; Alvarez, M.L., Sarachaga, I., Burgos, A., Estévez, E., Marcos, M., A Methodological Approach to Model-Driven Design and Development of Automation Systems (2017) IEEE Transactions on Automation Science and Engineering, 15, pp. 67-79. , https://doi.org/10.1109/TASE.2016.2574644; Andreopoulos, A., Tsotsos, J.K., 50 Years of Object Recognition: Directions Forward (2013) Computer Vision and Image Understanding, 117 (8), pp. 827-891; Arroyo, E., Fay, A., Chioua, M., Hoernicke, M., (2014), Integrating Plant and Process Information as a Basis for Automated Plant Diagis Tasks. Proc. IEEE Int. Conf. Emerging Technologies and Factory Automation (ETFA), September 2014; (2015), http://www.axiomtek.it/Download/Article/Download/trends_factory_automation_%20IoT_091115.pdf, Trends Factory Automation: The Internet of Things; Bakule, L., Decentralized Control: An Overview (2008) Annual Reviews in Control, 32, pp. 87-98; Bangemann, T., Karnouskos, S., Camp, R., Carlsson, O., State of the Art in Industrial Automation (2014) Industrial Cloud-Based Cyber-Physical Systems: The IMC-AESOP Approach, pp. 23-47. , Cham: Encyclopedia of Global Archaeology/Springer Verlag, and; Barth, M., Fay, A., Automated Generation of Simulation Models for Control Code Tests (2013) Control Engineering Practice, 21, pp. 218-230; Basanta-Val, P., (2017), An efficient industrial big-data engine. IEEE Trans. on Industrial Informatics, published online; Basanta-Val, P., Garcia-Valls, M., A Distributed Real-Time Java-Centric Architecture for Industrial Systems (2014) IEEE Transactions on Industrial Informatics, 10 (1), pp. 27-34; Bemporad, A., Heemels, M., Johansson, M., (2010), Networked control systems Lecture Notes Control and Information Sciences, 406, Springer, Berlin Heidelberg; Bratukhin, A., Sauter, T., Functional Analysis of Manufacturing Execution System Distribution (2011) IEEE Transactions on Industrial Informatics, 7 (4), pp. 740-749; Cai, H., Xu, L.D., Xu, B., Xie, C., Qin, S., Jiang, L., IoT-based Configurable Information Service Platform for Product Lifecycle Management (2014) IEEE Transactions on Industrial Informatics, 10 (2), pp. 1558-1567; Cena, G., Valenzano, A., Vitturi, S., Hybrid Wired/Wireless Networks for Realtime Communications (2008) IEEE Industrial Electronics Magazine, 2 (1), pp. 8-20; Cheminod, M., Durante, L., Valenzano, A., Review of Security Issues in Industrial Networks (2013) IEEE Transactions on Industrial Informatics, 9 (1), pp. 277-293; Chen, S.Y., Kalman Filter for Robot Vision: a Survey (2012) IEEE Transactions on Industrial Electronics, 59 (11), pp. 4409-4420; Chen, J.M., Cao, X.H., Cheng, P., Xiao, Y., Sun, Y.X., Distributed Collaborative Control for Industrial Automation with Wireless Sensor and Actuator Networks (2010) IEEE Transactions on Industrial Electronics, 57 (12), pp. 4219-4230; Chen, S., Li, Y., Ming Kwok, N., Active Vision in Robotic Systems: A Survey of Recent Developments (2011) The International Journal of Robotics Research, 30 (11), pp. 1343-1377; Chen, H.I., Shih, M.C., Visual Control of an Automatic Manipulation System by Microscope and Pneumatic Actuator (2013) IEEE Transactions on Automation Science and Engineering, 10 (1), pp. 215-218; Chen, S.Y., Zhang, J., Zhang, H., Kwok, N.M., Li, Y.F., Intelligent Lighting Control for Vision-Based Robotic Manipulation (2012) IEEE Transactions on Industrial Electronics, 59 (8), pp. 3254-3263; Colombo, A.W., Bangemann, T., Karnouskos, S., Delsing, J., Stluka, P., Harrison, R., Jammes, F., Martinez Lastra, J.L., (2014) Industrial Cloud-based Cyber-physical Systems, The IMC-AESOP Approach, , Cham: Springer, and, eds; Colombo, A.W., Schoop, R., Neubert, R., An Agent-Based Intelligent Control Platform for Industrial Holonic Manufacturing Systems (2006) IEEE Transactions on Industrial Electronics, 53 (1), pp. 322-337; Cucinotta, T., Mancina, A., Anastasi, G.F., Lipari, G., Mangeruca, L., Checcozzo, R., Rusina, F., A Real-Time Service-Oriented Architecture for Industrial Automation (2009) IEEE Transactions on Industrial Informatics, 5 (3), pp. 267-277; Dai, W., Dubinin, V.N., Vyatkin, V., Automatically Generated Layered Ontological Models for Semantic Analysis of Component-Based Control Systems (2013) IEEE Transactions on Industrial Informatics, 9 (4), pp. 2124-2136; De Pellegrini, F., Miorandi, D., Vitturi, S., Zanella, A., On the Use of Wireless Networks at Low Level of Factory Automation Systems (2006) IEEE Transactions on Industrial Informatics, 2 (2), pp. 129-143; Digani, V., Sabattini, L., Secchi, C., Fantuzzi, C., Ensemble Coordination Approach in Multi-AGV Systems Applied to Industrial Warehouses (2015) IEEE Transactions on Automation Science and Engineering, 12 (3), pp. 922-934; Dong, H.E., Hussain, F.K., Self-Adaptive Semantic Focused Crawler for Mining Services Information Discovery (2014) IEEE Transactions on Industrial Informatics, 10 (2), pp. 1616-1626; Dotoli, M., Fay, A., Miśkowicz, M., Seatzu, C., Advanced Control in Factory Automation: a Survey (2016) International Journal of Production Research, 55 (5), pp. 1243-1259; Eckert, K., Fay, A., Hadlich, T., Diedrich, C., Frank, T., Vogel-Heuser, B., Enhancing a Model-Based Engineering Approach for Distributed Manufacturing Automation Systems with Characteristics and Design Patterns (2015) Journal of Systems and Software, 101, pp. 221-235; Fay, A., Biffl, S., Winkler, D., Drath, R., Barth, M., (2013), A method to evaluate the openness of automation tools for increased interoperability. 39th Annual Conf. of the IEEE Industrial Electronics Society, 6844–6849; https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/255922/13-809-future-manufacturing-project-report.pdf, The Future of Manufacturing: A new era of opportunity and challenge for the UK, Project Report BIS/13/809, The Government Office for Science, London, October 2013; http://www.plattform-i40.de/I40/Redaktion/EN/Downloads/Publikation/plattform-i40-und-industrie-du-futur-joint-programme.pdf, Platform Industrie 4.0 & Alliance Industrie du Futur: Common List of Scenarios; http://www.plattform-i40.de/I40/Redaktion/EN/Downloads/Publikation/aspects-of-the-research-roadmap.html, Aspects of the Research Roadmap Application Scenarios; Grüner, S., Pfrommer, J., Palm, F., RESTful Industrial Communication with OPC UA (2016) IEEE Transactions on Industrial Informatics, 12 (5), pp. 1832-1841; Gue, K.R., Furmans, K., Seibold, Z., Uludağ, O., GridStore: a Puzzle-Based Storage System with Decentralized Control (2014) IEEE Transactions on Automation Science and Engineering, 11 (2), pp. 429-438; Gupta, R.A., Chow, M.Y., Networked Control Systems: Overview and Research Trends (2010) IEEE Transactions on Industrial Electronics, 57 (7), pp. 2527-2535; Hajduk, M., Jencík, P., Jeznýy, J., Vargovcík, L., Trends in Industrial Robotics Development (2013) Applied Mechanics and Materials, 282, pp. 1-6. , https://doi.org/10.4028/www.scientific.net/AMM.282.1; Heemels, W.P.M.H., Teel, A.R., van de Wouw, N., Nesic, D., Networked Control Systems with Communication Constraints: Tradeoffs Between Transmission Intervals, Delays and Performance (2010) IEEE Transactions on Automatic Control, 55 (8), pp. 1781-1796; Hespanha, J., Naghshtabrizi, P., Xu, Y., A Survey of Recent Results in Networked Control Systems (2007) Proceedings of the IEEE, 95 (1), pp. 138-162; Hildebrandt, C., Glawe, M., Müller, A.W., Fay, A., (2017), Reasoning on engineering knowledge: Applications and desired features. 14th Extended Semantic Web Conference (ESWC), Portoroz, Slovenia; Hildebrandt, C., Scholz, A., Fay, A., Schröder, T., Hadlich, T., Diedrich, C., Dubovy, M., Wiegand, R., (2017), Semantic Modeling for Collaboration and Cooperation of Systems the production domain. IEEE Int. Conf. Emerging Technologies and Factory Automation, Cyprus; (2012), A Report to the European Commission; Hwang, G.S., Lee, J.C., Park, J.W., Chang, T.-W., Developing Performance Measurement System for Internet of Things and Smart Factory Environment (2017) International Journal of Production Research, 55 (9), pp. 2590-2602; Iarovyi, S., Mohammed, W.M., Lobov, A., Ferrer, B.R., Martinez Lastra, J.L., Cyber–Physical Systems for Open-Knowledge-Driven Manufacturing Execution Systems (2016) Proceedings of the IEEE, 104 (5), pp. 1142-1154; Islam, K., Shen, W., Wang, X., Wireless Sensor Network Reliability and Security in Factory Automation: a Survey (2012) IEEE Transactions Systems, Man and Cybernetics–Part C: Applications and Reviews, 42 (6), pp. 1243-1256; Jindal, V., Verma, A.K., (2015), The underlying technologies WSNs: ZigBee vs. wireless HART. Proc. 12th Int. Conf. on Fuzzy Systems and Knowledge Discovery (FSKD), Zhangjiajie, China, 2208–2213; Jonsson, M., Kunert, K., Towards Reliable Wireless Industrial Communication with Real-Time Guarantees (2009) IEEE Transactions on Industrial Informatics, 5 (4), pp. 429-442; Julius, R., Schürenberg, M., Schumacher, F., Fay, A., Transformation of GRAFCET to PLC Code Including Hierarchical Structures (2017) Control Engineering Practice, 64, pp. 173-194; Kalogeras, A.P., Gialelis, J.V., Alexakos, C.E., Georgoudakis, M.J., Koubias, S.A., Vertical Integration of Enterprise Industrial Systems Utilizing Web Services (2006) IEEE Transactions on Industrial Informatics, 2 (2), pp. 120-128; Karnouskos, S., Leitão, P., Key Contributing Factors to the Acceptance of Agents in Industrial Environments (2017) IEEE Transactions on Industrial Informatics, 13 (2), pp. 696-703; Kehoel, B., Abbeel, S., Goldberg, K., A Survey of Research on Cloud Robotics and Automation (2015) IEEE Transactions on Automation Science and Engineering, 12 (2), pp. 398-409; Kotb, Y.T., Beauchemin, S.S., Barron, J.L., Workflow Nets for Multiagent Cooperation (2012) IEEE Transactions on Automation Science and Engineering, 9 (1), pp. 198-203; Ladiges, J., Fay, A., Holm, T., Hempen, U., Urbas, L., Obst, M., Albers, T., Integration of Modular Process Units Into Process Control Systems (2018) IEEE Transactions on Industry Applications, 54 (2), pp. 1870-1880; Ladiges, J., Fay, A., Lamersdorf, W., Automated Determining of Manufacturing Properties and Their Evolutionary Changes From Event Traces (2016) Intelligent Industrial Systems, 2 (2), pp. 163-178; Lange, J., (2011), https://industrial.softing.com/uploads/softing_downloads/FAe-JL-OPCUA-2011-04-etz_01.pdf, New automation concepts with OPC unified architecture; Lapp, H.-C., Hanisch, H.-M., A new DES Control Synthesis Approach Based on Structural Model Properties (2013) IEEE Transactions on Industrial Informatics, 9 (4), pp. 2340-2348; Lastra, J.L.M., Delamer, M., Semantic Web Services in Factory Automation: Fundamental Insights and Research Roadmap (2006) IEEE Transactions on Industrial Informatics, 2 (1), pp. 1-11; Lee, J., Bagheri, B., Kao, H.-A., A Cyber-Physical Systems Architecture for Industry 4.0-Based Manufacturing Systems (2015) Manufacturing Letters, 3, pp. 18-23; Lee, J., Jin, C., Liu, Y., Davari Ardakani, H., Introduction to Data-Driven Methodologies for Prognostics and Health Management (2017) Probabilistic Prognostics and Health Management of Energy Systems, pp. 9-32. , https://www.springer.com/in/book/9783319558516, Ekwaro-Osire S., Gonçalves A., Alemayehu F., (eds), Springer, and,. edited by; Leitão, P., Agent-based Distributed Manufacturing Control: A State-of-the-art Survey (2009) Engineering Applications of Artificial Intelligence, 22 (7), pp. 979-991; Leitão, P., Colombo, A.W., Karnouskos, S., Industrial Automation Based on Cyber-Physical Systems Technologies: Prototype Implementations and Challenges (2016) Computers in Industry, 81, pp. 11-25; Leitão, P., Marik, V., Vrba, P., Past, Present, and Future of Industrial Agent Applications (2013) IEEE Transactions on Industrial Informatics, 9 (4), pp. 2360-2372; Leitão, P., Rodrigues, N., Turrin, C., Pagani, A., Multiagent System Integrating Process and Quality Control in a Factory Producing Laundry Washing Machines (2015) IEEE Transactions on Industrial Informatics, 11 (4), pp. 879-886; Lian, F.-L., Moyne, J.R., Tilbury, D.M., Performance Evaluation of Control Networks: Ethernet, Control Net, and DeviceNet (2001) IEEE Control Systems Magazine, 21 (1), pp. 66-83; Liao, Y.X., Deschamps, F., de Freitas Rocha Loures, E., Pierin Ramos, L.F., Past, Present and Future of Industry 4.0 - a Systematic Literature Review and Research Agenda Proposal (2017) International Journal of Production Research, 55 (12), pp. 3609-3629; Liu, S., Liu, J., Feng, Y., Rong, G., Performance Assessment of Decentralized Control Systems: a Distributed Approach (2014) Control Engineering Practice, 22, pp. 252-263; Liu, S., Xu, D., Zhang, D., Zhang, Z., High Precision Automatic Assembly Based on Microscopic Vision and Force Information (2016) IEEE Transactions on Automation Science and Engineering, 13 (1), pp. 382-393; Liu, S., Zhang, J., Liu, J., Feng, Y., Rong, G., Distributed Model Predictive Control with Asynchronous Controller Evaluations (2013) The Canadian Journal of Chemical Engineering, 91, pp. 1609-1620; Livatino, S., Banno, F., Muscato, G., 3-D Integration of Robot Vision and Laser Data with Semiautomatic Calibration in Augmented Reality Stereoscopic Visual Interface (2012) IEEE Transactions on Industrial Informatics, 8 (1), pp. 69-77; Lu, Y., Industry 4.0: A Survey on Technologies, Applications and Open Research Issues (2017) Journal of Industrial Information Integration, 6, pp. 1-10; Lv, Z., Song, H., Basanta-Val, P., Next-generation big Data Analytics: State of the art, Challenges, and Future Research Topics (2017) IEEE Transactions on Industrial Informatics, 13 (4), pp. 1891-1899; Maione, B., Naso, D., Evolutionary Adaptation of Dispatching Agents in Heterarchical Manufacturing Systems (2001) International Journal of Production Research, 39 (7), pp. 1481-1503; Malamas, E.N., Petrakis, E.G.M., Zervakis, M., Petit, L., Legat, J.-D., A Survey on Industrial Vision Systems, Applications and Tools (2003) Image and Vision Computing, 21 (2), pp. 171-188; Manyika, J., Sinclair, J., Dobbs, R., Strube, G., Rassey, L., Mischke, J., Remes, J., (2012), http://www.mckinsey.com/insights/manufacturing/the_future_of_manufacturing, Manufacturing the future: the next era of global growth and innovation. McKinsey Global Institute., and, eds; McKinnon, C., Marshall, J.A., Automatic Identification of Large Fragments in a Pile of Broken Rock Using a Time-of-Flight Camera (2014) IEEE Transactions on Automation Science and Engineering, 11 (3), pp. 935-942; Merdan, M., Vallee, M., Lepuschitz, W., Zoitl, A., Monitoring and Diagnostics of Industrial Systems Using Automation Agents (2011) International Journal of Production Research, 49 (5), pp. 1497-1509; Metzger, M., Polakow, G., A Survey on Applications of Agent Technology in Industrial Process Control (2011) IEEE Transactions on Industrial Informatics, 7 (4), pp. 570-581; Millàn, P., Orihuela, L., Vivas, C., Rubio, F.R., Dimarogonas, D.V., Johansson, K.H., Sensor-network-based Robust Distributed Control and Estimation (2013) Control Engineering Practice, 21, pp. 1238-1249; Miskowicz, M.E., (2016) Event-Based Control and Signal Processing, , Boca Raton, FL: CRC Press; Moritz, G., Golatowski, F., Lerche, C., Timmermann, D., Beyond 6LoWPAN: Web Services in Wireless Sensor Networks (2013) IEEE Transactions on Industrial Informatics, 9 (4), pp. 1795-1805; Mukherjee, A., Physical-layer Security in the Internet of Things: Sensing and Communication Confidentiality Under Resource Constraints (2015) Proceedings of the IEEE, 103 (10), pp. 1747-1761; Ng, M.K., Ngan, H.Y.T., Yuan, X., Zhang, W., Patterned Fabric Inspection and Visualization by the Method of Image Decomposition (2014) IEEE Transactions on Automation Science and Engineering, 11 (3), pp. 943-947; Perutka, K., (2010), A survey of decentralized adaptive control, new trends technologies: Control, management, computational intelligence and network systems; : 978-953-307-213-5; (2010), https://ec.europa.eu/research/industrial_technologies/pdf/ppp-factories-of-the-future-strategic-multiannual-roadmap-info-day_en.pdf, Factories of the FutureP: strategic multi-annual roadmap, 2010, 40, Luxembourg; Puttonen, J., Lobov, A., Martinez Lastra, J.L., Semantics-based Composition of Factory Automation Processes Encapsulated by Web Services (2013) IEEE Transactions on Industrial Informatics, 9 (4), pp. 2349-2359; Runde, S., Fay, A., Software Support for Building Automation Requirements Engineering–An Application of Semantic Web Technologies in Automation (2011) IEEE Transactions on Industrial Informatics, 7 (4), pp. 723-730; Samaras, I.K., Hassapis, G.D., Gialelis, J.V., A Modified DPWS Protocol Stack for 6LoWPAN-Based Wireless Sensor Networks (2013) IEEE Transactions on Industrial Informatics, 9 (1), pp. 209-217; Sauter, T., The Three Generations of Field-Level Networks—Evolution and Compatibility Issues (2010) IEEE Transactions on Industrial Electronics, 57 (11), pp. 3585-3595; Scattolini, R., Architectures for Distributed and Hierarchical Model Predictive Control–a Review (2009) Journal of Process Control, 19 (5), pp. 723-731; Schreiber, S., Fay, A., (2011), A reference system for the benchmarking of manufacturing control systems. IEEE Conf. on Emerging Technologies and Factory Automation; Schütz, D., Wannagat, A., Legat, C., Vogel-Heuser, B., Development of PLC-Based Software for Increasing the Dependability of Production Automation Systems (2013) IEEE Transactions on Industrial Informatics, 9 (4), pp. 2397-2406; Secchi, C., Bonfè, M., Fantuzzi, C., On the use of UML for Modeling Mechatronic Systems (2007) IEEE Transactions on Automation Science and Engineering, 4 (1), pp. 105-113; Sha, M., Gunatilaka, D., Wu, C., Lu, C., Empirical Study and Enhancements of Industrial Wireless Sensor–Actuator Network Protocols (2017) IEEE Internet of Things Journal, 4 (3), pp. 696-704; Shin, S., Kwon, T., Jo, G.Y., Park, Y., Rhy, H., An Experimental Study of Hierarchical Intrusion Detection for Wireless Industrial Sensor Networks (2010) IEEE Transactions on Industrial Informatics, 6 (4), pp. 744-757; Stenmark, M., Malec, J., Nilsson, K., Robertsson, A., On Distributed Knowledge Bases for Robotized Small-Batch Assembly (2015) IEEE Transactions on Automation Science and Engineering, 12 (2), pp. 519-528; Su, J., Qiao, H., Ou, Z., Liu, Z.Y., Vision-based Caging Grasps of Polyhedron-Like Workpieces with a Binary Industrial Gripper (2015) IEEE Transactions on Automation Science and Engineering, 12 (3), pp. 1033-1046; Tabbara, M., Nesic, D., Teel, A.R., Stability of Wireless and Wireline Networked Control Systems (2007) IEEE Transactions on Automatic Control, 52 (9), pp. 1615-1630; Tang, Y., Peng, C., Yin, S., Qiu, J., Gao, H., Kaynak, O., Robust Model Predictive Control Under Saturations and Packet Dropouts with Application to Networked Flotation Processes (2014) IEEE Transactions on Automation Science and Engineering, 11 (4), pp. 1056-1064; Theiss, S., Vasyutynskyy, V., Kabitzsch, K., Software Agents in Industry: A Customized Framework in Theory and Praxis (2009) IEEE Transactions on Industrial Informatics, 5 (2), pp. 147-156; Theorin, A., Bengtsson, K., Provost, J., Lieder, M., Johnsson, C., Lundholm, T., An Event-Driven Manufacturing Information System Architecture for Industry 4.0 (2017) International Journal of Production Research, 55 (5), pp. 1297-1311; Therani, M., Ontology Development for Designing and Managing Dynamic Business Process Networks (2007) IEEE Transactions on Industrial Informatics, 3 (2), pp. 173-185; Thramboulidis, K., IEC 61499 in Factory Automation (2006) Advances in Computer, Information, and Systems Sciences, and Engineering, pp. 115-124. , https://link.springer.com/chapter/10.1007/1-4020-5261-8_20; http://www.manufacturing.gov/, A national advanced manufacturing portal; van Noten, J., Gadeyne, K., Vitters, M., Model-based Systems Engineering of Discrete Production Lines Using SysML: An Experience Report (2017) Procedia CIRP, 60, pp. 157-162; Vitturi, S., Tramarin, F., Energy Efficient Ethernet for Real-Time Industrial Networks (2015) IEEE Transactions on Automation Science and Engineering, 12 (1), pp. 228-237; Vogel-Heuser, B., Diedrich, C., Fay, A., Jeschke, S., Kowalewski, S., Wollschlaeger, M., Göhner, P., Challenges for Software Engineering in Automation (2014) Journal of Software Engineering and Applications, 7, pp. 440-451. , https://doi.org/10.4236/jsea.2014.75041; Vogel-Heuser, B., Obermeier, M., Braun, S., Sommer, K., Jobst, F., Schweizer, K., Evaluation of a UML-Based Versus an IEC 61131-3-Based Software Engineering Approach for Teaching PLC Programming (2013) IEEE Transactions on Education, 56 (3), pp. 329-335; Vrba, P., Tichý, P., Mařík, V., Hall, K.H., Staron, R.J., Maturana, F.P., Kadera, P., Rockwell Automation’s Holonic and Multiagent Control Systems Compendium (2011) IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 41 (1), pp. 14-30; Vyatkin, V., IEC 61499 as Enabler of Distributed and Intelligent Automation: State-of-the-art Review (2011) IEEE Transactions on Industrial Informatics, 7 (4), pp. 768-781; Vyatkin, V., Software Engineering in Industrial Automation: State-of-the-art Review (2013) IEEE Transactions on Industrial Informatics, 9 (3), pp. 1234-1249; Wang, F.-Y., Liu, D., (2008) Networked Control Systems. Theory and Applications, , London: Springer, and, eds; Willig, A., Recent and Emerging Topics in Wireless Industrial Communications: A Selection (2008) IEEE Transactions on Industrial Informatics, 4 (2), pp. 102-124; Wollschlaeger, M., Sauter, T., Jasperneite, J., The Future of Industrial Communication: Automation Networks in the era of the Internet of Things and Industry 4.0 (2017) IEEE Industrial Electronics Magazine, 11 (1), pp. 17-27; Xiao, G., Guo, J., Xu, L.D., Gong, Z., User Interoperability with Heterogeneous IoT Devices Through Transformation (2014) IEEE Transactions on Industrial Informatics, 10 (2), pp. 1486-1496; Yang, C.-H., Vyatkin, V., Pang, C., Model-driven Development of Control Software for Distributed Automation: A Survey and an Approach (2014) IEEE Transactions on Systems, Man, and Cybernetics: Systems, 44 (3), pp. 292-305; Yin, S., Kaynak, O., Big Data for Modern Industry: Challenges and Trends (2015) Proceedings of the IEEE, 103 (2), pp. 143-146; Zhang, L., Gao, H., Kaynak, O., Network-induced Constraints in Networked Control Systems - a Survey (2013) IEEE Transactions on Industrial Informatics, 9 (1), pp. 403-416; Zhang, Y., Qian, C., Lv, J., Liu, Y., Agent and Cyber-Physical System Based Self-Organizing and Self-Adaptive Intelligent Shopfloor (2017) IEEE Transactions on Industrial Informatics, 13 (2), pp. 737-747; Zhong, R.Y., Xu, C., Chen, C., Huang, G.Q., Big Data Analytics for Physical Internet-Based Intelligent Manufacturing Shop Floors (2017) International Journal of Production Research, 55 (9), pp. 2610-2621}, document_type={Review}, source={Scopus}, }`

### 2018

- Cavone, G., Dotoli, M., Epicoco, N., Morelli, D. & Seatzu, C. (2018) A Game-theoretical Design Technique for Multi-stage Supply Chains under Uncertainty IN IEEE International Conference on Automation Science and Engineering., 528-533.

[Bibtex]`@CONFERENCE{Cavone2018528, author={Cavone, G. and Dotoli, M. and Epicoco, N. and Morelli, D. and Seatzu, C.}, title={A Game-theoretical Design Technique for Multi-stage Supply Chains under Uncertainty}, journal={IEEE International Conference on Automation Science and Engineering}, year={2018}, volume={2018-August}, pages={528-533}, doi={10.1109/COASE.2018.8560501}, art_number={8560501}, note={cited By 4}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85059976158&doi=10.1109%2fCOASE.2018.8560501&partnerID=40&md5=029376a062b0c55a7cccfc170988ada1}, abstract={We present a design approach for multi-stage Supply Chains (SCs) that allows selecting candidates and assigning them orders under uncertainty. A bargaining game model in its extensive form (i.e., with a time sequencing of moves) and in a fuzzy setting is proposed. The product quantities that each actor requires from the previous SC stage are determined modelling the real behavior of SC stakeholders, which on the one hand act to maximize their own profit, on the other hand cooperate to maximize the overall efficiency of the SC and minimize production costs and lead times. Assignments are determined taking into account stock levels, uncertain production or warehouse capacities, and customers' demand. Thus, the method supports the decision making process providing an agile, cooperative, and resource-efficient design of multi-stage SCs under uncertain parameters. A literature SC is used as a test case to evaluate the effectiveness of the technique. © 2018 IEEE.}, keywords={Decision making; Supply chains; Uncertainty analysis, Bargaining game; Decision making process; Design approaches; Overall efficiency; Resource-efficient; Theoretical design; Uncertain parameters; Warehouse capacity, Game theory}, references={Esmaeili, M., Allameh, G., Tajvidi, T., Using game theory for analysing pricing models in closed-loop Supply Chain from short-and long-term perspectives (2016) Int. J. Prod. Res., 54 (7), pp. 2152-2169; Dubey, R., Gunasekaran, A., Childe, S.J., Papadopoulos, T., Blome, C., Luo, Z., Antecedents of resilient supply chains: An empirical study (2017) IEEE Transactions on Engineering Management, , In press; Dias, L.S., Ierapetritou, M.G., From process control to Supply Chain Management: An overview of integrated decision making strategies (2017) Comput. Chem. Eng., 106, pp. 826-835; Dotoli, M., Epicoco, N., Falagario, M., A fuzzy technique for supply chain network design with quantity discounts (2017) Int. J. Prod. Res., 55 (7), pp. 1862-1884; Karsak, E.E., Dursun, M., Taxonomy and review of nondeterministic analytical methods for supplier selection (2016) Int. J. Comput. Integr. Manuf., 29 (3), pp. 263-286; Mukherjee, K., Supplier selection criteria and methods: Past, present and future (2016) Int. J. Oper. Res., 27 (1-2), pp. 356-373; Simi, D., Kovaevi, I., Svirevi, V., Simi, S., 50 years of fuzzy set theory and models for supplier assessment and selection: A literature review (2017) J. Appl. Log., 24, pp. 85-96; Olesen, O.B., Petersen, N.C., Stochastic data envelopment analysis-a review (2016) Eur. J. Oper. Res., 251 (1), pp. 2-21; Dotoli, M., Epicoco, N., Falagario, M., Sciancalepore, F., A stochastic cross-efficiency Data Envelopment Analysis approach for supplier selection under uncertainty (2016) Int. Trans. Oper. Res., 23 (4), pp. 725-748; Govindan, K., Fattahi, M., Keyvanshokooh, E., Supply chain network design under uncertainty: A comprehensive review and future research directions (2017) Eur. J. Oper. Res., 263 (1), pp. 108-141; Leider, S., Lovejoy, W.S., Bargaining in supply chains (2016) Manage. Sci., 62 (10), pp. 3039-3058; Cachon, G.P., Netessine, S., Game theory in supply chain analysis (2004) Handbook of Quantitative Supply Chain Analysis, pp. 13-65; Nagarajan, M., Soši, G., Game-theoretic analysis of cooperation among Supply Chain agents: Review and extensions (2008) Eur. J. Oper. Res., 187 (3), pp. 719-745; Mohammaditabar, D., Ghodsypour, S.H., Hafezalkotob, A., A game theoretic analysis in capacity-constrained supplier-selection and cooperation by considering the total supply chain inventory costs (2016) Int. J. Prod. Econ., 181, pp. 87-97; Leng, M., Parlar, M., Game theoretic applications in Supply Chain Management: A review (2005) INFOR, 43 (3), pp. 187-220; Xu, J., Zhao, S., Noncooperative game-based equilibrium strategy to address the conflict between a construction company and selected suppliers (2017) J. Constr. Eng. Manag., 143 (8), pp. 1-10; Dotoli, M., Epicoco, N., Falagario, M., Sciancalepore, F., A crossefficiency fuzzy data envelopment analysis technique for performance evaluation of decision making units under uncertainty (2015) Comput. Ind. Eng., 79, pp. 103-114; Zimmermann, H.J., Fuzzy set theory (2010) Wiley Interdiscip. Rev. Comput. Stat., 2 (3), pp. 317-332}, document_type={Conference Paper}, source={Scopus}, }`

- Carli, R., Dotoli, M. & Pellegrino, R. (2018) Multi-criteria decision-making for sustainable metropolitan cities assessment. IN Journal of Environmental Management, 226.46-61.

[Bibtex]`@ARTICLE{Carli201846, author={Carli, R. and Dotoli, M. and Pellegrino, R.}, title={Multi-criteria decision-making for sustainable metropolitan cities assessment}, journal={Journal of Environmental Management}, year={2018}, volume={226}, pages={46-61}, doi={10.1016/j.jenvman.2018.07.075}, note={cited By 45}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85051362383&doi=10.1016%2fj.jenvman.2018.07.075&partnerID=40&md5=57606eb68995a362a44497e2b3089e36}, abstract={The recent development of metropolitan cities, especially in Europe, requires an effective integrated management of city services, infrastructure, and communication networks at a metropolitan level. A preliminary step towards a proper organizational and management strategy of the metropolitan city is the analysis, benchmarking and optimization of the metropolitan areas through a set of indicators coherent with the overall sustainability objective of the metropolitan city. This paper proposes the use of the Analytic Hierarchy Process multi-criteria decision making technique for application in the smart metropolitan city context, with the aim of analysing the sustainable development of energy, water and environmental systems, through a set of objective performance indicators. Specifically, the 35 indicators defined for the Sustainable Development of Energy, Water and Environment Systems Index framework are used. The application of the approach to the real case study of four metropolitan areas (Bari, Bitonto, Mola, and Molfetta) in the city of Bari (Italy) shows its usefulness for the local government in benchmarking metropolitan areas and providing decision indications on how to formulate the sustainable development strategy of the metropolitan city. Based on the Analytic Hierarchy Process characteristics, the results highlight that although one specific area (Mola in the considered case) is globally ranked at the first place, it is only ranked first with respect to some dimensions. Such a result has strong implications for the metropolitan city's manager who has the possibility to identify and implement targeted actions, which may be designed ad hoc to improve specific dimensions based on the current state of the city, thus maximizing the efficiency and effectiveness of the actions undertaken for the sustainable development of energy, water and environmental systems of the whole metropolitan city. © 2018 Elsevier Ltd}, author_keywords={Analytic hierarchy process; Multi-criteria decision making; Performance evaluation; Planning; Sustainable development}, keywords={analytical hierarchy process; assessment method; benchmarking; decision making; development strategy; local government; metropolitan area; multicriteria analysis; optimization; performance assessment; sustainability; sustainable development; urban planning, Article; benchmarking; city; decision making; energy resource; environmental sustainability; government; Italy; sustainable development; water supply; city; environmental protection; Europe, Bari [Bari (ADS)]; Bari [Puglia]; Italy; Puglia, Cities; Conservation of Natural Resources; Decision Making; Europe; Italy}, references={www.aqp.it/portal/pls/portal/docs/1/1282932.PDF; http://www.aeroportidipuglia.it; Afgan, N., Carvalho, M., Hovanov, N., Energy system assessment with sustainability indicators (2000) Energy Pol., 28, pp. 603-612; Afgan, N., Carvalho, M., Hovanov, N., Modeling of energy system sustainability Index (2005) Therm. Sci., 9 (2), pp. 3-16; Becker, W., Saisana, M., Paruolo, P., Vandecasteele, I., Weights and importance in composite indicators: closing the gap (2017) Ecol. Indicat., 80, pp. 12-22; Berardi, U., Sustainability assessments of communities through rating systems (2013) Environ. Dev. Sustain., 15 (6), pp. 1573-1591; Boselli, R., Cesarini, M., Mercorio, F., Mezzanzanica, M., (2015), Applying the AHP to smart mobility services: a case study. In DATA (pp. 354-361); Carli, R., Dotoli, M., Pellegrino, R., Ranieri, L., (2013), (October). Measuring and managing the smartness of cities: a framework for classifying performance indicators. In Proc. of Systems, Man, and Cybernetics (SMC) IEEE International Conference on (pp. 1288-1293). 2013; Carli, R., Deidda, P., Dotoli, M., Pellegrino, R., (2014), (September). An urban control center for the energy governance of a smart city. In Proc. of Emerging Technology and Factory Automation (ETFA) IEEE International Conference on (pp. 1-7). 2014; Carli, R., Dotoli, M., Pellegrino, R., A hierarchical decision making strategy for the energy management of smart cities (2017) IEEE Trans. Autom. Sci. Eng., 14 (2), pp. 505-523; http://www.cittametropolitana.ba.it/; http://www.comune.bari.it/portal/page/portal/bari/temiBari/ambienteEverde/pianoDiAzionePerLEnergiaSostenbilePaes; http://www.comune.bari.it; http://www.comune.bitonto.ba.it/paes.html; http://www.comune.bitonto.ba.it; http://www.comune.moladibari.ba.it/paes.html; http://www.comune.moladibari.ba.it; http://www.comune.molfetta.ba.it/trasparenza/piano-dazione-per-lenergia-sostenibile-paes/; http://www.comune.molfetta.ba.it; http://www.agenziacoesione.gov.it/opencms/export/sites/dps/it/documentazione/PON_metro/PON_Citta_Metropolitane.pdf; Dirks, S., Keeling, M., Dencik, J., (2009), How Smart Is Your City?: Helping Cities Measure Progress. IBM Institute for Business Value, IBM Global Business Services, New York; Elkarni, F., Mustafa, I., Increasing the utilization of solar energy technologies (SET) in Jordan: analytic Hierarchy Process (1993) Energy Pol., 21, pp. 978-984; (2005), http://www.geni.org/globalenergy/library/renewable-energy-resources/world/europe/hydro-europe/indexbig.shtml, European Communities e Global Energy Research Institute. Atlas of geothermal resources in Europe. In: Heat-flow Density; http://www.eea.europa.eu/themes/air/interactive/pm10; http://www.parks.it/regione.puglia; http://www.icitylab.it/il-rapporto-icityrate/cose/; Fujita, M., Krugman, P.R., Venables, A., (2001), The Spatial Economy: Cities, Regions and International Trade. MIT Press; Giffinger, R., Fertner, C., Kramar, H., Kalasek, R., Pichler-Milanović, N., Meijers, E., (2007), http://www.smartcities.eu/download/smart_cities_final_report.pdf, “Smart Cities: Ranking of European Medium-sized Cities”. Vienna, Austria: Centre of Regional Science (SRF), Vienna University of Technology. Available at; Giri, S., Nejadhashemi, A.P., Application of analytical hierarchy process for effective selection of agricultural best management practices (2014) J. Environ. Manag., 132, pp. 165-177; www.footprintnetwork.org; Hoekstra, A., Mekonnen, M., The water footprint of humanity (2012) Proc. Natl. Acad. Sci., 109 (9), pp. 3232-3237; Hoekstra, A., Chapagain, A., Aldaya, M., (2011), Water Footprint Assessment Manual: Setting the Global Standard. Earthscan, London; Hsieh, H.N., Chou, C.Y., Chen, C.C., Chen, Y.Y., (2011), The evaluating indices and promoting strategies of intelligent city in Taiwan. In Multimedia Technology (ICMT) International Conference on (pp. 6704-6709). IEEE. 2011; Inkoom, J.N., Frank, S., Greve, K., Fürst, C., A framework to assess landscape structural capacity to provide regulating ecosystem services in West Africa (2018) J. Environ. Manag., 209, pp. 393-408; http://ottomilacensus.istat.it/provincia/072/; http://re.jrc.ec.europa.eu/pvgis/apps4/pvest.php#; Kim, H.M., Han, S.S., City profile: Seoul (2012) Cities, 29 (2), pp. 142-154; Kılkış, Ş., Composite index for benchmarking local energy systems of Mediterranean port cities (2015) Energy, 92, pp. 622-638; Kılkış, Ş., Sustainable development of energy, water and environment systems index for Southeast European cities (2016) J. Clean. Prod., 130, pp. 222-234; Kılkış, Ş., Benchmarking south east european cities with the sustainable development of energy, water and environment systems index (2018) J. Sustain. Dev. Energy Water Environ. Syst., 6 (1), pp. 162-209; Kılkış, Ş., Sustainable development of energy, water and environment systems (SDEWES) index for policy learning in cities (2018) Int. J. Innovat. Sustain. Dev., 12 (1-2), pp. 87-134; http://www.miur.gov.it/web/guest/home; Nardo, M., Saisana, M., Saltelli, A., Tarantola, S., (2005), Tools for Composite Indicators Building. European Communities; http://maps.nrel.gov/swera; http://maps.nrel.gov/swera; Neirotti, P., De Marco, A., Cagliano, A.C., Mangano, G., Scorrano, F., Current trends in Smart City initiatives: some stylised facts (2014) Cities, 38, pp. 25-36; Papa, R., Gargiulo, C., Battarra, R., Niglio, R., Fabricatti, K., Pappalardo, G., Tremiterra, M.R., Carlin, M., (2016), Città Metropolitane e Smart Governance Iniziative di successo e nodi critici verso la Smart City. Smart City, Urban Planning for a Sustainable Future, vol. 1. FedOAPress - Federico II Open Access University Press, Napoli, Italy; Saaty, T.L., (1980), The Analytical Hierarchy Process. NY McGraw Hill; Saaty, T.L., Decision making with the analytic hierarchy process (2008) Int. J. Serv. Sci., 1 (1), pp. 83-98; Saisana, M., Tarantola, S., (2002), reportState-of-the-art Report on Current Methodologies and Practices for Composite Indicator Development (p. 214). European Commission, Joint Research Centre, Institute for the Protection and the Security of the Citizen, Technological and Economic Risk Management Unit; Saisana, M., Saltelli, A., Tarantola, S., Uncertainty and sensitivity analysis techniques as tools for the quality assessment of composite indicators (2005) J. Roy. Stat. Soc. Ser. A (Stat. Soc.), 168 (2), pp. 307-323; Saltelli, A., Chan, K., Scott, M., (2000), Sensitivity Analysis, Probability and Statistics Series (New York: John Wiley & Sons); Saltelli, A., Tarantola, S., Campolongo, F., Sensitivity analysis as an ingredient of modelling (2000) Stat. Sci., 15, pp. 377-395; (2008), Saltelli, Andrea, et al. Global Sensitivity Analysis: the Primer. John Wiley & Sons; Saltelli, A., Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index (2010) Comput. Phys. Commun., 181 (2), pp. 259-270; http://www.scimagojr.com/countryrank.php; http://www.siemens.com/entry/cc/en/greencityindex; Sobol, I.M., Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates (2001) Math. Comput. Simulat., 55 (1-3), pp. 271-280; Tan, S., Yang, J., Yan, J., Lee, C., Hashim, H., Chen, B., A holistic low carbon city indicator framework for sustainable development (2017) Appl. Energy, 185, pp. 1919-1930; Tian, W., Bai, J., Sun, H., Zhao, Y., Application of the analytic hierarchy process to a sustainability assessment of coastal beach exploitation: a case study of the wind power projects on the coastal beaches of Yancheng, China (2013) J. Environ. Manag., 115, pp. 251-256; Toppeta, D., (2010), http://www.thinkinnovation.org/file/research/23/en/Toppeta_Report_005_2010.pdf, The Smart City Vision: How Innovation and ICT Can Build Smart, “Livable” Sustainable Cities: the Innovation Knowledge Foundation. Available at; Urbaniec, K., Mikulčić, H., Duić, N., Lozano, R., SDEWES 2014–sustainable development of energy, water and environment systems (2016) J. Clean. Prod., 130, pp. 1-11; Urbaniec, K., Mikulčić, H., Rosen, M.A., Duić, N., A holistic approach to sustainable development of energy, water and environment systems (2017) J. Clean. Prod., 155, pp. 1-11; Väisänen, S., Havukainen, J., Sokka, L., Luoranen, M., Horttanainen, M., Using a multi-method approach for decision-making about a sustainable local distributed energy system: a case study from Finland (2016) J. Clean. Prod., 137, pp. 1330-1338; Wang, G., Qin, L., Li, G., Chen, L., Landfill site selection using spatial information technologies and AHP: a case study in Beijing, China (2009) J. Environ. Manag., 90 (8), pp. 2414-2421; Wang, L., Long, R., Chen, H., Study of urban energy performance assessment and its influencing factors based on improved stochastic frontier analysis: a case study of provincial capitals in China (2017) Sustainability, 9 (7), p. 1110; Wang, L., Yuan, G., Long, R., Chen, H., An urban energy performance evaluation system and its computer implementation (2017) J. Environ. Manag., 204, pp. 684-694; Wang, M., Sun, Y., Sweetapple, C., Optimization of storage tank locations in an urban stormwater drainage system using a two-stage approach (2017) J. Environ. Manag., 204, pp. 31-38; Washburn, D., Sindhu, U., Balaouras, S., Dines, R.A., Hayes, N.M., Nelson, L.E., (2010), http://public.dhe.ibm.com/partnerworld/pub/smb/smarterplanet/forr_help_cios_und_smart_city_initiatives.pdf, Helping CIOs Understand “Smart City” Initiatives: Defining the Smart City, its Drivers, and the Role of the CIO. Cambridge, MA: Forrester Research, Inc. Available at; Wilson, D.C., Rodic, L., Cowing, M.J., Velis, C.A., Whiteman, A.D., Scheinberg, A., Vilches, R., Oelz, B., ‘Wasteaware’ benchmark indicators for integrated sustainable waste management in cities (2015) Waste Manag., 35, pp. 329-342; Yajie, D., Beicheng, X., Weidong, C., Carbon footprint of urban areas: an analysis based on emission sources account model (2014) Environ. Sci. Pol., 44, pp. 181-189; Zaman, A., Lehmann, S., The zero waste index: a performance measurement tool for waste management systems in a ‘zero waste city’ (2013) J. Clean. Prod., 50, pp. 123-132}, document_type={Article}, source={Scopus}, }`

- Carli, R., Dotoli, M. & Epicoco, N. (2018) Cost-Optimal Energy Scheduling of a Smart Home under Uncertainty IN 2018 IEEE Conference on Control Technology and Applications, CCTA 2018., 1668-1673.

[Bibtex]`@CONFERENCE{Carli20181668, author={Carli, R. and Dotoli, M. and Epicoco, N.}, title={Cost-Optimal Energy Scheduling of a Smart Home under Uncertainty}, journal={2018 IEEE Conference on Control Technology and Applications, CCTA 2018}, year={2018}, pages={1668-1673}, doi={10.1109/CCTA.2018.8511345}, art_number={8511345}, note={cited By 1}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85056899368&doi=10.1109%2fCCTA.2018.8511345&partnerID=40&md5=df2f65f9300879823bcc53a5aada2f14}, abstract={We present a novel energy scheduling approach under uncertain data for smart homes taking into account the presence of controllable electrical loads, renewable energy sources, dispatchable energy generators, and energy storage systems. The problem is stated as a fuzzy linear programming and is aimed at minimizing energy costs. The proposed approach allows managing the use of electrical devices, plan the energy production and supplying, and program the storage charging and discharging profiles under uncertain data. The method is validated through a literature case study showing its effectiveness in exploiting the potential of local energy generation and storage and in reducing the energy consumption costs, while limiting the peak average ratio of the energy profiles and complying with the user's energy needs. © 2018 IEEE.}, keywords={Automation; Energy utilization; Intelligent buildings; Linear programming; Renewable energy resources; Scheduling, Dispatchable energies; Electrical devices; Electrical load; Energy productions; Energy storage systems; Fuzzy linear programming; Minimizing energy; Renewable energy source, Digital storage}, references={Hubert, T., Grijalva, S., Realizing smart grid benefits requires energy optimization algorithms at residential level (2011) Proc. Innovative Smart Grid Technologies, 8; Schiefer, M., Smart home definition and security threats (2015) Proc. 9th Int. Conf. IT Security Incident Management & IT Forensics, pp. 114-118; Dotoli, M., Epicoco, N., Falagario, M., Sciancalepore, F., Costantino, N., A Nash equilibrium simulation model for the competitiveness evaluation of the auction based day ahead electricity market (2014) Comput. Ind., 65 (4), pp. 774-785; Kailas, A., Cecchi, V., Mukherjee, A., A survey of contemporary technologies for smart home energy management (2013) Green Information and Communication Systems Handbook, , Academic Press; Liu, Y., Qiu, B., Fan, X., Zhu, H., Han, B., Review of smart home Energy Management Systems (2016) Energy Procedia, 104, pp. 504-508; Carli, R., Dotoli, M., Energy scheduling of a smart home under nonlinear pricing (2014) 53rd IEEE Ann. Conf. Decision and Control (CDC); Mohsenian-Rad, A.-H., Wong, V.W.S., Jatskevich, J., Schober, R., Optimal and autonomous incentive-based energy consumption scheduling algorithm for smart grid (2010) Proc. Innovative Smart Grid Technologies, p. 8; Theo, W.L., Lim, J.S., Ho, W.S., Hashim, H., Lee, C.T., Review of distributed generation (DG) system planning and optimization techniques: Comparison of numerical and mathematical modelling methods (2017) Renew. Sust. Energy Rev., 67, pp. 531-573; Sinha, S., Chandel, S.S., Review of recent trends in optimization techniques for solar photovoltaic-wind based hybrid energy systems (2015) Renew. Sust. Energy Rev., 50, pp. 755-769; Matho, T., Mukherjee, V., Energy storage systems for mitigating the variability of isolated hybrid power system (2015) Renew. Sust. Energy Rev., 51, pp. 1564-1577; Khan, I., Mahmood, A., Javaid, N., Razzaq, S., Khan, R.D., Ilahi, M., Home Energy Management Systems in future smart grids (2013) J. Basic. Appl. Sci. Res., 3 (3), pp. 1224-1231; Huang, Y., Wang, L., Guo, W., Kang, Q., Wu, Q., Chance constrained optimization in a home Energy Management System (2018) IEEE Trans. Smart Grid, 9 (1); Zeng, Y., Cai, Y., Huang, G., Dai, J., A review on optimization modeling of energy systems planning and GHG emission mitigation under uncertainty (2011) Energies, 4, pp. 1624-1656; Beaudin, M., Zareipour, H., Home energy management systems: A review of modelling and complexity (2015) Renew. Sust. Energy Rev., 45, pp. 318-335; Geramifar, H., Shahabi, M., Barforoshi, T., Coordination of energy storage systems and DR resources for optimal scheduling of microgrids uncer uncertainty (2017) IET Renew. Power Gen., 11, pp. 378-388; Nikmehr, N., Najafi-Ravadanegh, S., Khodaei, A., Probabilistic optimal scheduling of networked microgrids considering time-based demand response programs under uncertainty (2017) Appl. Energy, 198, pp. 267-279; Chen, H., Zhang, R., Bai, L., Jiang, T., Li, G., Jia, H., Li, X., Stochastic scheduling of Integrated energy systems considering wind power and multi-energy loads uncertainties (2017) J. Energ. Eng., 143; Liang, R.H., Liao, J.H., A fuzzy-optimization approach for generation scheduling with wind and solar energy systems (2007) IEEE Trans. Pow. Sys., 22, pp. 1665-1674; Chaouachi, A., Kamel, R.M., Andoulsi, R., Nagasaka, K., Multiobjective intelligent energy management for a microgrid (2013) IEEE Trans. Ind. Electron., 60, pp. 1688-1699; Shukla, A.K., Nath, R., Muhuri, P.K., Energy efficient task scheduling with type-2 fuzzy uncertainty (2015) IEEE Int. Conf. Fuzzy Syst.; Suo, C., Li, Y.P., Wang, C.X., Yu, L., A type-2 fuzzy chance constrained programming method for planning Shangai's energy system (2017) Int. J. Electr. Power Energy Syst., 90, pp. 37-53; Dotoli, M., Epicoco, N., Falagario, M., Sciancalepore, F., A crossefficiency fuzzy Data Envelopment Analysis technique for performance evaluation of decision making units under uncertainty (2015) Comput Ind Eng, 79, pp. 103-114; Zimmermann, H.J., Fuzzy set theory and its applications Kluwer Academic Publishers, Boston/Dordrecht/London, 2001. , 4th Ed}, document_type={Conference Paper}, source={Scopus}, }`

- Carli, R. & Dotoli, M. (2018) A Decentralized Control Strategy for the Energy Management of Smart Homes with Renewable Energy Exchange IN 2018 IEEE Conference on Control Technology and Applications, CCTA 2018., 1662-1667.

[Bibtex]`@CONFERENCE{Carli20181662, author={Carli, R. and Dotoli, M.}, title={A Decentralized Control Strategy for the Energy Management of Smart Homes with Renewable Energy Exchange}, journal={2018 IEEE Conference on Control Technology and Applications, CCTA 2018}, year={2018}, pages={1662-1667}, doi={10.1109/CCTA.2018.8511617}, art_number={8511617}, note={cited By 1}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85056821403&doi=10.1109%2fCCTA.2018.8511617&partnerID=40&md5=85a9dde2ca8fa544e4b4a5901580188f}, abstract={This paper presents a decentralized control strategy for the scheduling of energy activities of interconnected smart homes that purchase energy from a supplier while exchanging renewable energy produced by individually owned distributed energy resources. The scheduling problem is solved with a twofold design objective. First, the model aims at reducing the overall energy supply from the grid, by allowing users to borrow/lend some amount of renewable energy from/to other users. Second, the problem is formulated to optimally plan users' controllable loads. We assume a time-varying quadratic pricing of the energy purchased from the distribution network. The proposed solution is based on a decentralized optimization algorithm combining parametric optimization with the proximal Jacobian Alternating Direction Method of Multipliers. The application of the proposed technique to a simulated case study under several scenarios shows its effectiveness. © 2018 IEEE.}, keywords={Automation; Energy resources; Intelligent buildings; Scheduling, Alternating direction method of multipliers; Controllable loads; Decentralized optimization; Design objectives; Distributed Energy Resources; Parametric optimization; Renewable energies; Scheduling problem, Decentralized control}, references={Carli, R., Dotoli, M., Pellegrino, R., A hierarchical decision-making strategy for the energy management of smart cities (2017) IEEE Trans. Autom. Sci. Eng., 14 (2), pp. 505-523; Carli, R., Dotoli, M., Pellegrino, R., Ranieri, L., A decision making technique to optimize a buildings' stock energy efficiency (2017) IEEE Trans. Syst. Man, Cybern. Syst., 47 (5), pp. 794-807; Alizadeh, M., Chang, T.H., Scaglione, A., Grid integration of distributed renewables through coordinated demand response (2012) Proceedings of the IEEE Conference on Decision and Control, pp. 3666-3671; Liu, T., Tan, X., Sun, B., Wu, Y., Guan, X., Tsang, D.H.K., Energy management of cooperative microgrids with P2P energy sharing in distribution networks (2016) 2015 IEEE International Conference on Smart Grid Communications, SmartGridComm 2015, pp. 410-415; Palensky, P., Dietrich, D., Demand side management: Demand response, intelligent energy systems, and smart loads (2011) Ind. Informatics, IEEE Trans., 7 (3), pp. 381-388; Lakshminarayana, S., Quek, T.Q.S., Poor, H.V., Cooperation and storage tradeoffs in power grids with renewable energy resources (2014) IEEE J. Sel. Areas Commun., 32 (7), pp. 1386-1397; Barbato, A., Capone, A., Carello, G., Delfanti, M., Merlo, M., Zaminga, A., House energy demand optimization in single and multiuser scenarios (2011) 2011 IEEE International Conference on Smart Grid Communications, SmartGridComm 2011, pp. 345-350; Deng, R., Yang, Z., Chen, J., Asr, N.R., Chow, M.Y., Residential energy consumption scheduling: A coupled-constraint game approach (2014) IEEE Trans. Smart Grid, 5 (3), pp. 1340-1350; Mohsenian-Rad, A.H., Wong, V.W.S., Jatskevich, J., Schober, R., Leon-Garcia, A., Autonomous demand-side management based on game-theoretic energy consumption scheduling for the future smart grid (2010) IEEE Trans. Smart Grid, 1 (3), pp. 320-331; Hermans, R., Almassalkhi, M., Hiskens, I., Incentive-based coordinated charging control of plug-in electric vehicles at the distribution-transformer level (2012) American Control Conference, pp. 264-269; Xu, Y., Pan, F., Tong, L., Dynamic scheduling for charging electric vehicles: A priority rule (2016) IEEE Trans. Automat. Contr., 61 (12), pp. 4094-4099; Carli, R., Dotoli, M., Member, S., (2017) A Distributed Control Algorithm for Waterfilling of Networked Control Systems Via Consensus, 1 (2), pp. 334-339; Carli, R., Dotoli, M., A decentralized control strategy for optimal charging of electric vehicle fleets with congestion management (2017) 2017 IEEE International Conference on Service Operations and Logistics, and Informatics, (1), pp. 63-67; Espinosa, L.A.D., Almassalkhi, M., Hines, P., Frolik, J., Aggregate Modeling and Coordination of Diverse Energy Resources under Packetized Energy Management ? (2017) 2017 56th IEEE Conf. Decis. Control, pp. 1394-1400; Wu, Y., Lau, V.K.N., Tsang, D.H.K., Qian, L.P., Meng, L., Optimal energy scheduling for residential smart grid with centralized renewable energy source (2014) IEEE Syst. J., 8 (2), pp. 562-576; Carli, R., Dotoli, M., A decentralized resource allocation approach for sharing renewable energy among interconnected smart homes (2015) Proceedings of the IEEE Conference on Decision and Control, pp. 5903-5908. , 54rd IEEE; Carli, R., Dotoli, M., Cooperative distributed control for the energy scheduling of smart homes with shared energy storage and renewable energy source (2017) IFAC-PapersOnLine, 50 (1); Zhu, T., Huang, Z., Sharma, A., Su, J., Irwin, D., Mishra, A., Menasche, D., Shenoy, P., Sharing renewable energy in smart microgrids (2013) 2013 ACM/IEEE International Conference on Cyber-Physical Systems, ICCPS 2013, pp. 219-228; Huang, Z., Zhu, T., Gu, Y., Irwin, D., Mishra, A., Shenoy, P., Minimizing electricity costs by sharing energy in sustainable microgrids (2014) Proceedings of the 1st ACM Conference on Embedded Systems for Energy-Efficient Buildings-BuildSys '14, pp. 120-129; Zhong, W., Huang, Z., Zhu, T., Gu, Y., Zhang, Q., Yi, P., Jiang, D., Xiao, S., Ides: Incentive-driven distributed energy sharing in sustainable microgrids (2015) 2014 International Green Computing Conference, IGCC 2014; Alskaif, T., Zapata, M.G., Bellalta, B., Nilsson, A., A distributed power sharing framework among households in microgrids: A repeated game approach (2017) Computing, 99 (1), pp. 23-37; Carli, R., Dotoli, M., Energy scheduling of a smart home under nonlinear pricing (2014) Proceedings of the IEEE Conference on Decision and Control, , 2015-Febru February; Attivissimo, F., Di Nisio, A., Lanzolla, A.M.L., Paul, M., Feasibility of a photovoltaic-thermoelectric generator: Performance analysis and simulation results (2015) IEEE Trans. Instrum. Meas., 64 (5), pp. 1158-1169; Carli, R., Dotoli, M., Energy scheduling of a smart home under nonlinear pricing (2014) Proceedings of the IEEE Conference on Decision and Control, pp. 5648-5653. , 2015-Febru February; Sanchez-Squella, A., Ortega, R., Grino, R., Malo, S., Dynamic energy router (2010) IEEE Control Syst. Mag., 30 (6), pp. 72-80; Bertsekas, D., (1999) Nonlinear Programming.; Boyd, S., Parikh, N., Chu, E., Peleato, B., Eckstein, J., Distributed optimization and statistical learning via the alternating direction method of multipliers (2010) Found. Trends Mach. Learn., 3 (1), pp. 1-122; Giselsson, P., Boyd, S., Linear convergence and metric selection for douglas-rachford splitting and admm (2017) IEEE Trans. Automat. Contr., 62 (2), pp. 532-544; Deng, W., Lai, M.J., Peng, Z., Yin, W., Parallel multi-block admm with o(1 / k) convergence (2017) J. Sci. Comput., 71 (2), pp. 712-736}, document_type={Conference Paper}, source={Scopus}, }`

- Carli, R., Dotoli, M. & Pellegrino, R. (2018) A decision-making tool for energy efficiency optimization of street lighting. IN Computers and Operations Research, 96.223-235.

[Bibtex]`@ARTICLE{Carli2018223, author={Carli, R. and Dotoli, M. and Pellegrino, R.}, title={A decision-making tool for energy efficiency optimization of street lighting}, journal={Computers and Operations Research}, year={2018}, volume={96}, pages={223-235}, doi={10.1016/j.cor.2017.11.016}, note={cited By 45}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85044511260&doi=10.1016%2fj.cor.2017.11.016&partnerID=40&md5=f655fabb0054cefa3a05bcc0c63059f4}, abstract={This paper develops a multi-criteria decision making tool to support the public decision maker in optimizing energy retrofit interventions on existing public street lighting systems. The related literature analysis clearly highlights that, to date, only a few number of studies deal with the definition of optimal decision strategies complying with multiple and conflicting objectives in the planning of street lighting refurbishment. To fill this gap, we propose a decision making tool that allows deciding, in an integrated way, the optimal energy retrofit plan in order to simultaneously reduce energy consumption, maintain comfort, protect the environment, and optimize the distribution of actions in subsystems, while ensuring an efficient use of public funds. The presented tool is applied to a real street lighting system of a wide urban area in Bari, Italy. The obtained results highlight that the approach effectively supports the city energy manager in the refurbishment of the street lighting systems. © 2017 Elsevier Ltd}, author_keywords={Energy efficiency management; Multi-criteria optimization; Public street lighting}, keywords={Decision making; Energy utilization; Lighting fixtures; Multiobjective optimization; Retrofitting; Street lighting; Urban planning, Conflicting objectives; Efficiency managements; Energy efficiency optimizations; Multi criteria decision making; Multicriteria optimization; Optimal decision strategy; Reduce energy consumption; Street lighting system, Energy efficiency}, references={Achterberg, T., SCIP: solving constraint integer programs,” (2009) Math. Progr. Comp., 1, pp. 1-41; Beccali, M., Improvement of energy efficiency and quality of street lighting in South Italy as an action of sustainable energy action plans. The case study of Comiso (RG) (2015) Energy, 92, pp. 394-408; Berardi, L., Giustolisi, O., Savic DA An operative approach to water distribution system rehabilitation (2009) Environmental & Water Resources Congress 2009 - Great Rivers, May 17-21, pp. 238-250. , S. Starrett CD-ROM Edition ASCE Publisher Kansas City, Missouri, USA; Burgos-Payan, M., Correa-Moreno, F., Riquelme-Santos, J., Improving the energy efficiency of street lighting (2012) A case in the South of Spain, European Energy Market (EEM), 2012 9th International Conference on the, pp. 1-8; Cai, Y., Sanstad, A.H., Model uncertainty and energy technology policy: the example of induced technical change (2016) Comput. Oper. Res., 66, pp. 362-373; Carli, R., Dotoli, M., Andria, G., Lanzolla, A.M.L., Bi-level programming for the strategic energy management of a smart city (2016) 2016 IEEE Workshop on Environmental, Energy, and Structural Monitoring Systems (EESMS), Bari, pp. 1-6; Carli, R., Dotoli, M., Pellegrino, R., ICT and optimization for the energy management of smart cities: the street lighting decision panel (2014) Emerging Technology and Factory Automation (ETFA), 2014 IEEE, , vol., no.16-19. pp. 1–7; Carli, R., Dotoli, M., Pellegrino, R., A hierarchical decision making strategy for the energy management of smart cities (2017) IEEE TASE (Trans. Autom. Sci. Eng.), 14 (2), pp. 505-523; Carli, R., Dotoli, M., Pellegrino, R., Ranieri, L., Using multi-objective optimization for the integrated energy efficiency improvement of a smart city public buildings’ portfolio (2015) Automation Science and Engineering (CASE), 2015 IEEE International Conference on, , vol., no.24-28. 21–26; Carli, R., Dotoli, M., Pellegrino, R., Ranieri, L., A decision making technique to optimize a building stock energy efficiency (2017) IEEE SMC-A (Trans. Syst. Man Cybern.), 47 (5), pp. 794-807; Coureaux, I.M., Manzano, E., The energy impact of luminaire depreciation on urban lighting (2013) Energy Sustainable Dev., 17 (4), pp. 357-362; Covitti, A., Delvecchio, G., Neri, F., Ripoli, A., Labini, M.S., Road lighting installation design to optimize energy use by genetic algorithms. Computer as a tool (2005) EUROCON 2005. The International Conference on, 2, pp. 1541-1544; Dotoli, M., Fanti, M.P., Mangini, A.M., Fuzzy multi-objective optimization for network design of integrated e-supply chains (2007) Int. J. Comput. Integr. Manuf., 20 (6), pp. 588-601; Figueira, J., Greco, S., Ehrgott, M., Multiple Criteria Decision Analysis: State of the Art Surveys (2005), Springer Boston; Gómez-Lorente, D., Rabaza, O., Espín, A., Peña-García, A., (2013), Optimization of efficiency and energy saving in public lighting with multi-objective evolutionary algorithms. International Conference on Renewable Energies and Power Quality (ICREPQ’13), Bilbao (Spain), 20th to 22th March; Huang, S.C., Lee, L.L., Jeng, M.S., Hsieh, Y.C., Assessment of energy-efficient LED street lighting through large-scale demonstration (2012) Renewable Energy Research and Applications (ICRERA), 2012 International Conference on, pp. 1-5. , 11-14; Hwang, C.L., Yoon, K., Multiple Attribute Decision Making: Methodsand Applications (1981), Springer-Verlag New York, NY, USA; Ishizaka, A., Nemery, P., Multi-Criteria Decision Analysis: Methods and Software (2013), Wiley Chichester, U.K; Jiménez, A., Ríos-Insua, S., Mateos, A., A generic multi-attribute analysis system (2006) Comput. Oper. Res., 33 (4), pp. 1081-1101; Jollands, N., Waide, P., Ellis, M., The 25 IEA energy efficiency policy recommendations to the G8 gleneagles plan of action (2010) Energy Policy, 38, pp. 6409-6418; Kostic, M., Djokic, L., Pojatar, D., Strbac-Hadzibegovic, N., Technical and economic analysis of road lighting solutions based on mesopic vision (2009) Build. Environ., 44 (1), pp. 66-75; Lagorse, J., Paire, D., Miraoui, A., Sizing optimization of a stand-alone street lighting system powered by a hybrid system using fuel cell, PV and battery (2009) Renewable Energy, 34 (3), pp. 683-691; Lobão, J.A., Devezas, T., Catalão, J.P.S., Energy efficiency of lighting installations: software application and experimental validation (2015) Energy Rep., 1, pp. 110-115; Mahapatra, S., Chanakya, H.N., Dasappa, S., Evaluation of various energy devices for domestic lighting in India: technology, economics and CO 2 emissions (2009) Energy Sustainable Dev., 13 (4), pp. 271-279; Mahlia, T.M.I., Razak, H.A., Nursahida, M.A., Life cycle cost analysis and payback period of lighting retrofit at the University of Malaya (2011) Renewable Sustainable Energy Rev., 15 (2), pp. 1125-1132; Marler, R.T., Arora, J.S., Survey of multi-objective optimization methods for engineering (2004) Struct. Multidisc. Optim., 26 (6), pp. 369-395; Martello, S., Toth, P., Knapsack Problems: Algorithms and Computer Implementations (1990), John Wiley & Sons, Inc; Nahapetyan, A.G., Bilinear programming (2008) Encyclopedia of Optimization, pp. 279-282. , Springer US; Narisada, K., Schreuder, D., (2004) Light Pollution Handbook, 322. , Springer Science & Business Media; Orzáez, M.J.H., de Andrés Díaz, J.R., Comparative study of energy-efficiency and conservation systems for ceramic metal-halide discharge lamps (2013) Energy, 52, pp. 258-264; Parise, G., Martirano, L., Cecchini, G., Design and energetic analysis of an advanced control upgrading existing lighting systems (2013) IEEE Ind. Commer. Power Syst. Tech. Conf. (I&CPS), 49, pp. 1-8; Pataki, G.E., DeIorio, V.A., Flynn, W.M., NYSERDA How-to guide to Effective Energy-Efficient Street Lighting for Planners and Engineers (2012); Rabaza, O., Palomares-Muñoz, Z.E., Peña-García, A., Gómez-Lorente, D., Arán-Carrión, J., Aznar-Dols, F., Espín-Estrella, A., Multi-objective optimization applied to photovoltaic street lighting systems (2014) International Conference on Renewable Energies and Power Quality (ICREPQ’14), , ISSN, No.12; Radulovic, D., Skok, S., Kirincic V Energy efficiency public lighting management in the cities (2011) Energy, 36 (4), pp. 1908-1915; Ramadhani, F., Bakar, K.A., Shafer, M.G., Optimization of standalone street light system with consideration of lighting control (2013) Technological Advances in Electrical, Electronics and Computer Engineering (TAEECE), 2013 International Conference on, pp. 583-588. , 9-11; Rea, M.S., The IESNA Lighting Handbook (2000), Illuminating Engineering Society of North America New York; Sȩdziwy, A., Kotulski, L., Towards highly energy-efficient roadway lighting (2016) Energies, 9 (4), p. 263; Siddiqui, A.A., Ahmad, A.W., Yang, H.K., Lee, C., ZigBee based energy efficient outdoor lighting control system (2012) 2012 14th International Conference Advanced Communication Technology, ICACT, pp. 916-919; Sperber, A.N., Elmore, A.C., Crow, M.L., Cawlfield, J.D., Performance evaluation of energy efficient lighting associated with renewable energy applications (2012) Renewable Energy, 44, pp. 423-430; Tan, B., Yavuz, Y., Otay, E.N., Çamlıbel, E., Optimal selection of energy efficiency measures for energy sustainability of existing buildings (2016) Comput. Oper. Res., 66, pp. 258-271; Torfi, F., Farahani, R.Z., Rezapour, S., Fuzzy AHP to determine the relative weights of evaluation criteria and fuzzy TOPSIS to rank the alternatives (2010) Appl. Soft Comput., 10 (2), pp. 520-528; OPTI-A free MATLAB toolbox for optimization (2017), http://www.i2c2.aut.ac.nz/Wiki/OPTI/, Available at; Velik, R., Nicolay, P., Energy management in storage-augmented, grid-connected prosumer buildings and neighborhoods using a modified simulated annealing optimization (2016) Comput. Oper. Res., 66, pp. 248-257; Wu, Y., Shi, C., Zhang, X., Yang, W., Design of new intelligent street light control system (2010) Control and Automation (ICCA), 2010 8th IEEE International Conference on, pp. 1423-1427. , 9-11; Xiao, H., Kang, Q., Zhao, J., Xiao, Y.S., A dynamic sky recognition method for use in energy efficient lighting design based on CIE standard general skies (2010) Build. Environ., 45 (5), pp. 1319-1328; Yan, W., Hui, S.R., Chung, H.S.H., Energy saving of large-scale high-intensity-discharge lamp lighting networks using a central reactive power control system (2009) Ind. Electron. IEEE Trans., 56 (8), pp. 3069-3078; Yang, T., Hung, C.C., Multiple-attribute decision making methods for plant layout design problem (2007) Robot. Comput. Integr. Manuf., 23 (1), pp. 126-137; Zanakis, S.H., Solomon, A., Wishart, N., Dublish, S., Multi-attribute decision making: a simulation comparison of select methods (1998) Eur. J. Oper. Res., 107 (3), pp. 507-529; Zha, Y., Zhao, L., Bian, Y., Measuring regional efficiency of energy and carbon dioxide emissions in China: a chance constrained DEA approach (2016) Comput. Oper. Res., 66, pp. 351-361; Zhang, W., Reimann, M., A simple augmented ε-constraint method for multi-objective mathematical integer programming problems (2014) Eur. J. Oper. Res., 234 (1), pp. 15-24}, document_type={Article}, source={Scopus}, }`

- Cavone, G., Dotoli, M. & Seatzu, C. (2018) A Survey on Petri Net Models for Freight Logistics and Transportation Systems. IN IEEE Transactions on Intelligent Transportation Systems, 19.1795-1813.

[Bibtex]`@ARTICLE{Cavone20181795, author={Cavone, G. and Dotoli, M. and Seatzu, C.}, title={A Survey on Petri Net Models for Freight Logistics and Transportation Systems}, journal={IEEE Transactions on Intelligent Transportation Systems}, year={2018}, volume={19}, number={6}, pages={1795-1813}, doi={10.1109/TITS.2017.2737788}, note={cited By 30}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85029185229&doi=10.1109%2fTITS.2017.2737788&partnerID=40&md5=b13acc8aca9a368189e296cec63c8a37}, abstract={The benefits of logistics and transportation systems to citizens, economy, and society can strongly increase when considering a smart, safe, and environmentally friendly management. This results in the implementation of intelligent transportation systems that combine innovative technologies and transportation frameworks at the aim of finding proper solutions to the related decision problems. To achieve such a goal, the intrinsic discrete event dynamics of these systems should be considered when deriving a model to be used for simulation, analysis, optimization, and control. Among the different discrete event models, Petri Nets (PNs) are particularly effective due to a series of relevant features. In addition, several high-level PN models (e.g., colored, continuous, or hybrid) allow the solution of complex and large-dimension problems that typically arise from real-life applications in the area of freight logistics and transportation systems. This paper presents a survey on contributions in this area. Papers are classified according to the addressed problem, namely, strategic/tactical or operational decision-making-level problem, and the adopted PN formalism. We also debate the approaches' viability, discussing contributions and limitations, and identify future research directions to enhance the successful application of PNs in freight logistics and transportation systems. © 2000-2011 IEEE.}, author_keywords={control; Freight transportation; management; modeling and simulation; optimization; Petri nets}, keywords={Analytical models; Containers; Control engineering; Decision making; Discrete event simulation; Intelligent systems; Logistics; Management; Optimization; Petri nets; Random processes; Stochastic models; Stochastic systems; Surveys; Transportation, Discrete event dynamics; Future research directions; Intelligent transportation systems; Logistics and transportations; Model and simulation; Object oriented model; Operational decision making; Real-life applications, Freight transportation}, references={Degano, C., Di Febbraro, A., On using Petri nets to detect faulty behaviours in an intermodal container terminal (2002) Proc. 9th Meet. Euro Work. Group Transp., Intermodality, Sustain. Intell. Transp. Syst., p. 6. , http://ieeexplore.ieee.org/document/1167943/; Rama, D., Andrews, J.D., Railway infrastructure asset management: The whole-system life cost analysis (2016) IET Intell. Transport Syst., 10 (1), pp. 58-64; Dotoli, M., Epicoco, N., Falagario, M., Cavone, G., A timed Petri nets model for performance evaluation of intermodal freight transport terminals (2016) IEEE Trans. Autom. Sci. Eng., 13 (2), pp. 842-857. , Apr; Dotoli, M., Fanti, M.P., An urban traffic network model via coloured timed Petri nets (2006) Control Eng. Pract., 14 (10), pp. 1213-1229. , Oct; Febbraro, A.D., Giglio, D., Sacco, N., Urban traffic control structure based on hybrid Petri nets (2004) IEEE Trans. Intell. Transp. Syst., 5 (4), pp. 224-237. , Dec; Gudelj, A., Krcum, M., Twrdy, E., Models and methods for operations in port container terminals (2010) PROMET-Traffic Transp., 22 (1), pp. 43-51; Kim, K.H., Kim, H.B., The optimal sizing of the storage space and handling facilities for import containers (2002) Transp. Res. B, Methodol., 36 (9), pp. 821-835. , Nov; Imai, A., Nishimura, E., Hattori, M., Papadimitriou, S., Berth allocation at indented berths for mega-containerships (2007) Eur. J. Oper. Res., 179 (2), pp. 579-593. , Jun; Garrido, R., Allendes, F., Modeling the internal transport system in a containerport (2002) Transp. Res. Rec., J. Transp. Res. Board, 1782, pp. 84-91. , http://trrjournalonline.trb.org/doi/abs/10.3141/1782-10, Jan; Liu, J., Du, Y., Li, P., Hong, Y., Petri-net modeling of container-port work flo (2013) Inf. Technol. J., 12 (9), pp. 1845-1850; Di Febbraro, A., Sacco, N., Sensitivity analysis of the performances of seaport container terminals (2013) Proc. Int. Conf. Control, Decision Inf. Technol. (CoDIT), pp. 691-696. , Hammamet, Tunisia. May; Li, Y., Pan, L., Liu, X., Modeling and simulation of full logistics process of the port based on Petri nets (2010) Proc. 2nd IEEE Int. Conf. Inf. Manage. Eng., pp. 24-27. , Chengdu, China. Apr; Wang, W., Song, X., Tang, C., Liu, Y., System simulation of capacity for container terminal based on stochastic Petri net (2008) Proc. IEEE Int. Conf. Autom. Logistics, pp. 2889-2892. , Qingdao, China. Sep; Zhang, F.A.H., Jiang, S.B.Z., Modeling and analysis of container terminal logistics system by extended generalized stochastic Petri nets (2006) Proc. IEEE Int. Conf. Service Oper. Logistics, Inform., pp. 310-315. , Shanghai, China. Jun; Legato, P., Trunfio, R., Meisel, F., Modeling and solving rich quay crane scheduling problems (2012) Comput. Operat. Res., 39 (9), pp. 2063-2078. , Sep; Trunfio, R., A timed Petri net model for the quay crane scheduling problem (2014) Proc. 28th Eur. Conf. Modelling Simulation, pp. 441-447. , May; Kefi, M., Korbaa, O., Ghédira, K., Yim, P., Formalising an agentbased container stacking model via Petri nets (2006) Proc. Inf. Control Problems Manuf, 12 (1), pp. 357-362; Kezic, D., Matic, P., Racic, N., P-invariant based Petri net traffic controller (2009) Proc. 17th Medit. Conf. Control Autom., pp. 1096-1101. , Thessaloniki, Greece. Jun; Zou, J., Wei, Y., Wu, W., Petri nets model of emergency control system for shipping (2012) Proc. 9th IEEE Int. Conf. Netw., Sens. Control, pp. 295-300. , Beijing, China. Apr; Li, S., Cai, Q., Zhu, B., Research on maintenance processes modeling techniques for ships based on Petri nets (2009) Proc. 2nd Int. Conf. Power Electron. Intell. Transp. Syst., pp. 197-200. , Shenzhen, China. Dec; Bjørk, J., Hagalisletto, A.M., Challenges in simulating railway systems using Petri nets (2005) Precise Model. Anal., , Univ. Oslo, Oslo, Norway, Tech. Rep; Hagalisletto, A.M., Bjork, J., Yu, I.C., Enger, P., Constructing and refining large-scale railway models represented by Petri nets (2007) IEEE Trans. Syst., Man, Cybern. C, Appl. Rev., 37 (4), pp. 444-460. , Jul; Wu, D., Schnieder, E., Scenario-based modeling of the on-board of a satellite-based train control system with colored Petri nets (2016) IEEE Trans. Intell. Transp. Syst., 17 (11), pp. 3045-3061. , Nov; Durmuş, M.S., Söylemez, M.T., Railway signalization and interlocking design via automation Petri nets (2009) Proc. 7th Asian Control Conf., pp. 1558-1563. , Hong Kong Aug; Durmuş, M.S., Söylemez, M.T., Automation Petri net based railway interlocking and signalization design (2009) Proc. Int. Symp. Innov. Intell. Syst. Appl., pp. 12-16; Yldrm, U., Durmuş, M.S., Söylemez, M.T., (2010) Fail-safe Signalization and Interlocking Design for A Railway Yard: An Automation Petri Net Approach, pp. 1-2. , Dept. Control Eng., Istanbul Tech. Univ., Istanbul, Turkey; Söylemez, M.T., Durmuş, M.S., Yldrm, U., Türk, S., Sonat, A., The application of automation theory to railway signalization systems: The case of Turkish national railway signalization project (2011) Proc. 18th IFAC World Congr., pp. 10752-10757. , http://folk.ntnu.no/skoge/prost/proceedings/ifac11-proceedings/data/html/papers/3755.pdf, Aug; Durmuş, M.S., Yldrm, U., Söylemez, M.T., The application of automation theory to railway signaling systems: Turkish national railway signaling project (2013) Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 19 (5), pp. 216-223. , http://dergipark.gov.tr/pajes/issue/20498/218240; Vanit-Anunchai, S., Modelling railway interlocking tables using coloured Petri nets (2010) Coordination Models and Languages, pp. 137-151. , Berlin, Germany: Springer; Vanit-Anunchai, S., Verification of railway interlocking tables using coloured Petri nets (2009) Proc. Workshop Pract. Coloured Petri Nets CPN Tools, pp. 139-158. , http://cs.au.dk/fileadmin/site_files/cs/research_areas/centers_and_projects/cpn/paper08.pdfand, https://www.researchgate.net/profile/Somsak-Vanit-Anunchai/citations; Giua, A., Seatzu, C., Modeling and supervisory control of railway networks using Petri nets (2008) IEEE Trans. Autom. Sci. Eng., 5 (3), pp. 431-445. , Jul; Weng, Y.-S., Huang, Y.-S., Pan, Y.-L., Jeng, M., Design of traffic safety control systems for railroads and roadways using timed Petri nets (2015) Asian J. Control, 17 (2), pp. 626-635. , Mar; Fanti, M.P., Giua, A., Seatzu, C., Monitor design for colored Petri nets: An application to deadlock prevention in railway networks (2006) Control Eng. Pract., 14 (10), pp. 1231-1247; Guo, W., Zhao, H., Zhou, G., Verification and analysis of RSSP-1 protocol based on colored Petri nets (2012) Proc. 2nd Int. Conf. Bus. Comput. Global Inf., pp. 592-595. , Shanghai, China Oct; Jia, W., Mau, B., Ho, T., Liu, H., Yang, B., Bottlenecks detection of track allocation schemes at rail stations by Petri nets (2009) J. Transp. Syst. Eng. Inf. Technol., 9 (6), pp. 136-141; Cheng, Y.-H., Yang, L.-A., A fuzzy Petri nets approach for railway traffic control in case of abnormality: Evidence from Taiwan railway system (2009) Expert Syst. Appl., 36 (4), pp. 8040-8048; Ng, K.M., Reaz, M.B.I., Ali, M.A.M., A review on the applications of Petri nets in modeling, analysis, and control of urban traffic (2013) IEEE Trans. Intell. Transp. Syst., 14 (2), pp. 858-870. , Jun; Qu, Y., Li, L., Liu, Y., Chen, Y., Dai, Y., Travel routes estimation in transportation systems modeled by Petri nets (2010) Proc. IEEE Int. Conf. Veh. Electron. Safety, pp. 73-77. , Qingdao, China Jul; Aized, T., Srai, J.S., Hierarchical modelling of Last Mile logistic distribution system (2014) Int. J. Adv. Manuf. Technol., 70 (5-8), pp. 1053-1061; Franke, H., Dangelmaier, W., Decentralized management for transportation-logistics: A multi agent based approach (2003) Integr. Comput.-Aided Eng. Arch., 10 (2), pp. 203-210; Yuanchun, Y., Haoxue, L., Yong, Z., Evaluation emergency transport performances in response for hazardous materials road transportation accident based on SPN: A case of Jiangsu (2010) Proc. Int. Conf. Optoelectron. Image Process., pp. 509-513. , Hainan, China Nov; Centrone, G., Ukovich, W., Fanti, M.P., Iacobellis, G., A colored Petri net model of motorways for risk evaluation of HAZMAT transportation (2011) Proc. Int. Conf. Syst., Man, Cybern., pp. 562-567. , Anchorage, AK, USA Oct; Fanti, M.P., Iacobellis, G., Ukovich, W., A risk assessment framework for Hazmat transportation in highways by colored Petri nets (2015) IEEE Trans. Syst., Man, Cybern. Syst., 45 (3), pp. 485-495. , Mar; Kabashkin, I., Heuristic based decision support system for choice of alternative routes in the large-scale transportation transit system on the base of Petri net model (2016) Procedia Eng., 134, pp. 359-364. , Jan; Júlvez, J.J., Boel, R.K., A continuous Petri net approach for model predictive control of traffic systems (2010) IEEE Trans. Syst., Man, Cybern. A, Syst. Hum., 40 (4), pp. 686-697. , Jul; Dotoli, M., Fanti, M.P., Iacobellis, G., A freeway traffic control model by first order hybrid Petri nets (2011) Proc. IEEE Conf. Autom. Sci. Eng. (CASE), pp. 425-431. , Trieste, Italy Aug; Dotoli, M., Freeway traffic control via route guidance: An approach based on a first order hybrid Petri nets model (2012) Proc. 4th IFAC Conf. Anal. Design Hybrid Syst., p. 6. , Eindhoven, The Netherlands; Fanti, M.P., Iacobellis, G., Mangini, A.M., Ukovich, W., Freeway traffic modeling and control in a first-order hybrid Petri net framework (2014) IEEE Trans. Autom. Sci. Eng., 11 (1), pp. 90-102. , Jan; Demongodin, I., Modeling and analysis of transportation networks using batches Petri nets with controllable batch speed (2009) Applications and Theory of Petri Nets, pp. 204-222. , G. Franceschini and K. Wolf, Eds. Berlin, Germany: Springer; Ding, J., Chen, T., Xu, T., Propagated analysis of airport delays based on timed Petri nets (2009) Proc. Int. Conf. Comput. Intell. Secur., pp. 608-614. , Beijing, China Dec; Yang, B., Wei-Hong, L., Capability evaluation of air cargo export handling system using stochastic Petri net (2010) Proc. Int. Conf. Logistics Syst. Intell. Manage., pp. 1589-1593. , Harbin, China Jan; Davidrajuh, R., Binshan, L., Exploring airport traffic capability using Petri net based model (2011) Expert Syst. Appl., 38 (9), pp. 10923-10931; Lee, C., Huang, H.C., Liu, B., Xu, Z., Development of timed colour Petri net simulation models for air cargo terminal operations (2006) Comput. Ind. Eng., 51 (1), pp. 102-110; Oberheid, H., Söffker, D., Cooperative arrival management in air traffic control-A coloured Petri net model of sequence planning (2008) Applications and Theory of Petri Nets (Lecture Notes in Computer Science), 5062, pp. 348-367. , https://link.springer.com/chapter/10.1007/978-3-540-68746-7_23, K. M. van Hee and R. Valk, Eds. Berlin, Germany: Springer-Verlag; Werther, B., Moehlenbrink, C., Rudolph, M., Colored Petri net based formal airport control model for simulation and analysis of airport control processes (2007) Digital Human Modeling (Lecture Notes in Computer Science), 4561, pp. 1027-1036. , https://link.springer.com/chapter/10.1007/978-3-540-73321-8_115, Berlin, Germany: Springer-Verlag; Podgórski, P., Skorupski, J., Aircraft taxi route choice in case of conflict points existence (2016) Proc. 16th Int. Conf. Transp. Syst. Telematics (TST), pp. 366-377; Jamal, M., Zafar, N.A., Formalizing air traffic control system using agent-based mobile Petri nets (2015) Proc. Int. Conf. Inf. Commun. Technol., pp. 1-6. , Karachi, Pakistan Dec; Chou, H.-H., Chang, C.-T., Petri-net-based strategy to synthesize the operating procedures for cleaning pipeline networks (2005) Ind. Eng. Chem. Res., 44 (1), pp. 114-123; Lai, J.-W., Chou, H.-H., Chang, C.-T., Petri-net based integer programs for synthesizing optimal material-transfer procedures in pipeline networks (2006) J. Chin. Inst. Eng., 29 (2), pp. 337-346; Xiong, Y., Si, W., Wu, X., Analysis of emergency response for accident of oil and gas pipeline based on stochastic Petri net (2016) Proc. Anal. Modeling Simulation Conf. (ISCRAM), pp. 1-7; Gursesli, O., Desrochers, A.A., Modeling infrastructure interdependencies using Petri nets (2003) Proc. IEEE Int. Conf. Syst., Man, Cybern., 3, pp. 1506-1512. , Oct; Ren, H., Wang, Z., Shi, W., Performance modeling of asynchronous pipelines-An overview (2012) Procedia Eng., 29, pp. 3788-3793. , Jan; Guo, Y., Meng, X., Wang, D., Meng, T., Liu, S., He, R., Comprehensive risk evaluation of long-distance oil and gas transportation pipelines using a fuzzy Petri net model (2016) J. Natural Gas Sci. Eng., 33, pp. 18-29. , Jul; Wu, N., Zhou, M., Chu, F., A Petri net-based heuristic algorithm for realizability of target refining schedule for oil refinery (2008) IEEE Trans. Autom. Sci. Eng., 5 (4), pp. 661-676. , Oct; Wu, N., Chu, F., Chu, C., Zhou, M., Short-term schedulability analysis of multiple distiller crude oil operations in refinery with oil residency time constraint (2008) IEEE Trans. Syst., Man, Cybern. C, Appl. Rev., 39 (1), pp. 1-16. , Dec; Wu, N., Chu, F., Chu, C., Zhou, M., Hybrid Petri net modeling and schedulability analysis of high fusion point oil transportation under tank grouping strategy for crude oil operations in refinery (2010) IEEE Trans. Syst., Man, Cybern. C, Appl. Rev., 40 (2), pp. 159-175. , Mar; Wu, N., Chu, C., Chu, F., Zhou, M., Schedulability analysis of shortterm scheduling for crude oil operations in refinery with oil residency time and charging-tank-switch-overlap constraints (2011) IEEE Trans. Autom. Sci. Eng., 8 (1), pp. 190-204. , Jan; Wu, N., Zhu, M., Bai, L., Li, Z., Short-term scheduling of crude oil operations in refinery with high-fusion-point oil and two transportation pipelines (2014) Enterprise Inf. Syst., 10 (6), pp. 581-610; Wu, N., Zhou, M., Li, Z., Short-term scheduling of crude-oil operations: Enhancement of crude-oil operations scheduling using a Petri net-based control-theoretic approach (2015) IEEE Robot. Autom. Mag., 22 (2), pp. 64-76. , Jun; Kadri, H., Zouari, B., Shortest path search in dynamic reliability space: Hierarchical coloured Petri nets model and application to a pipeline network (2014) Proc. 16th Int. Conf. Comput. Modelling Simulation, pp. 254-259. , Mar; Kadri, H., Zouari, B., A high-level Petri nets approach for multiobjective optimization in pipeline networks (2014) Proc. Int. Conf. Simulation Modeling Methodol. Technol. Appl., pp. 211-218. , Vienna, Austria Aug; Dotoli, M., Fanti, M.P., Mangini, A.M., Stecco, G., Ukovich, W., The impact of ICT on intermodal transportation systems: A modelling approach by Petri nets (2010) Control Eng. Pract., 18 (8), pp. 893-903; Dotoli, M., Zgaya, H., Russo, C., Hammadi, S., A multi-agent advanced traveler information system for optimal trip planning in a co-modal framework (2017) IEEE Trans. Intell. Transp. Syst., 18 (9), pp. 2397-2412. , Sep; Eng-Larsson, F., Kohn, C., Modal shift for greener logistics-The shipper's perspective (2012) Int. J. Phys. Distrib. Logistics Manage., 42 (1), pp. 36-59; Filipova, K., Stojadinova, T., Hadjiatanasova, V., Application of Petri nets for transport streams modeling (2002) Facta Univ., Archit. Civil Eng., 2 (4), pp. 295-306; Di Febbraro, A., Porta, G., Sacco, N., A Petri net modelling approach of intermodal terminals based on Metrocargo system (2006) Proc. IEEE Int. Conf. Intell. Transp. Syst. Conf., pp. 1442-1447. , Toronto, ON, Canada Sep; Fischer, M., Kemper, P., Modeling and analysis of a freight terminal with stochastic Petri nets (2000) Proc. 9th IFAC Symp. Control Transp. Syst., 2, pp. 295-300. , Braunschweig, Germany; Maione, G., Ottomanelli, M., A preliminary Petri net model of the transshipment processes in the Taranto container terminal (2005) Proc. 10th IEEE Conf. Emerg. Technol. Factory Autom., p. 171. , Catania, Italy, Sep; Maione, G., Mangini, A.M., Ottomanelli, M., A generalized stochastic Petri net approach for modeling activities of human operators in intermodal container terminals (2016) IEEE Trans. Autom. Sci. Eng., 13 (4), pp. 1504-1516. , Oct; Cavone, G., Dotoli, M., Seatzu, C., Resource planning of intermodal terminals using timed Petri nets (2016) Proc. 13th Int. Workshop Discrete Event Syst. (WODES), pp. 44-50. , Xi'an, China, May/Jun; Kabashkin, I., Modelling of regional transit multimodal transport accessibility with Petri net simulation (2015) Procedia Comput. Sci., 77, pp. 151-157. , Dec; Degano, C., Di Febbraro, A., Modelling automated material handling in intermodal terminals (2001) Proc. IEEE/ASME Int. Conf. Adv. Intell. Mechatronics, pp. 1023-1028. , Como, Italy Jul; Degano, C., Fornara, P., Di Febbraro, A., Fault diagnosis in an intermodal container terminal (2001) Proc. Int. Conf. Emerg. Technol. Factory Autom., pp. 433-440. , Antibes-Juan les Pins, France Oct; Degano, C., Pellegrino, A., Multi-agent coordination and collaboration for control and optimization strategies in an intermodal container terminal (2002) Proc. Int. Eng. Manage. Conf., pp. 590-595. , Aug; Silva, C.A., Soares, C.G., Signoret, J.P., Intermodal terminal cargo handling simulation using Petri nets with predicates (2015) Proc. Inst. Mech. Eng., M, J. Eng. Maritime Environ., 229 (4), pp. 323-339; Cavone, G., Dotoli, M., Seatzu, C., Management of intermodal freight terminals by first-order hybrid Petri nets (2016) IEEE Robot. Autom. Lett., 1 (1), pp. 2-9. , Jan; Mahi, F., Nait-Sidi-Moh, A., Debbat, F., Khelfi, M.-F., Modelling and control of a multimodal transportation system using hybrid Petri nets with fuzzy logic (2013) Int. J. Syst. Control Commun., 5 (3-4), pp. 255-275; Yan, J., Li, L., Fault-tolerant controller design for automated guided vehicle systems based on Petri nets (2012) Proc. 15th Int. Conf. Intell. Transp. Syst., pp. 1531-1536. , Anchorage, AK, USA Sep; Gudelj, A., Kezíc, D., Vidacíc, S., Planning and optimization of AGV jobs by Petri net and genetic algorithm (2012) J. Inf. Org. Sci., 36 (2), pp. 99-122. , Jul; Liu, C., Ioannou, P., Petri net modeling and analysis of automated container terminal using automated guided vehicle systems (2002) Transp. Res. Rec., J. Transp. Res. Board, 1782, pp. 73-83. , http://trrjournalonline.trb.org/doi/abs/10.3141/1782-09, Jan; Nishi, T., Maeno, R., Petri net decomposition approach to optimization of route planning problems for AGV systems (2010) IEEE Trans. Autom. Sci. Eng., 7 (3), pp. 523-537. , Jul; Nishi, T., Tanaka, Y., Petri net decomposition approach for dispatching and conflict-free routing of bidirectional automated guided vehicle systems (2012) IEEE Trans. Syst., Man, Cybern. A, Syst., Humans, 42 (5), pp. 1230-1243. , Sep; Dotoli, M., Fanti, M.P., Coloured timed Petri net model for real time control of AGV systems (2004) Int. J. Prod. Res., 42 (9), pp. 1787-1814. , May; Wu, N., Zhou, M., Resource-oriented Petri nets in deadlock avoidance of AGV systems (2001) Proc. Int. Conf. Robot. Autom., pp. 64-69. , May; Wu, N.Q., Zhou, M., Modeling and deadlock control of automated guided vehicle systems (2004) IEEE/ASME Trans. Mechatronics, 9 (1), pp. 50-57. , Mar; Wu, N., Zhou, M., Shortest routing of bidirectional automated guided vehicles avoiding deadlock and blocking (2007) IEEE/ASME Trans. Mechatronics, 12 (1), pp. 63-72. , Feb; Roszkowska, E., Undirected colored Petri net for modelling and supervisory control of AGV systems (2002) Proc. 6th Int. Workshop Discrete Event Syst., pp. 135-142. , Oct; Giglio, D., A Petri net model for an open path multi-AGV system (2014) Proc. 11th Int. Conf. Inf. Control, Autom. Robot., pp. 734-745. , Vienna, Austria Sep}, document_type={Article}, source={Scopus}, }`

- Guan, X., Zhao, Q., Jia, S. Q. -S. & Dotoli, M. (2018) Welcome message from general and program chairs IN IEEE International Conference on Automation Science and Engineering., 1-3.

[Bibtex]`@CONFERENCE{Guan20181, author={Guan, X. and Zhao, Q. and Jia, S.Q.-S. and Dotoli, M.}, title={Welcome message from general and program chairs}, journal={IEEE International Conference on Automation Science and Engineering}, year={2018}, volume={2017-August}, pages={1-3}, doi={10.1109/COASE.2017.8256063}, note={cited By 0}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85044941166&doi=10.1109%2fCOASE.2017.8256063&partnerID=40&md5=7944ac6ea24984f7a7a4e1f04ef0bd32}, document_type={Editorial}, source={Scopus}, }`

- Cavone, G., Dotoli, M., Epicoco, N. & Seatzu, C. (2018) Efficient Resource Planning of Intermodal Terminals under Uncertainty , 398-403.

[Bibtex]`@CONFERENCE{Cavone2018398, author={Cavone, G. and Dotoli, M. and Epicoco, N. and Seatzu, C.}, title={Efficient Resource Planning of Intermodal Terminals under Uncertainty}, year={2018}, volume={51}, number={9}, pages={398-403}, doi={10.1016/j.ifacol.2018.07.065}, note={cited By 2}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85050140566&doi=10.1016%2fj.ifacol.2018.07.065&partnerID=40&md5=edaa20f412201b8f7e7839c4fc246aad}, abstract={This paper presents a decision support tool for the efficient resource planning and management of intermodal terminals under uncertainty, allowing to address the planning issue under imprecise or uncertain data (e.g., estimates on flows, resource utilization, operating conditions). The procedure consists of three steps: 1) the definition of a Timed Petri Net model of the terminal; 2) the computation of suitable performance indices to evaluate whether the current configuration is able to cope with a foreseen increase in the freight flows; 3) in the case of not satisfactory values of the indices at the previous step, the simulation of alternative planning solutions and the detection of the most efficient one via a cross-efficiency fuzzy Data Envelopment Analysis technique. In order to test its effectiveness, the procedure is applied to a real case study. © 2018}, author_keywords={Data Envelopment Analysis; efficiency; fuzzy theory; intermodal terminals; performance evaluation; Petri Nets; resource planning; uncertainty}, keywords={Computation theory; Data envelopment analysis; Decision support systems; Efficiency; Petri nets; Resource allocation; Uncertainty analysis, Fuzzy theory; Intermodal terminals; performance evaluation; Resource planning; uncertainty, Information management}, references={Aruldoss, M., Miranda Lakshmi, T., Prasanna Venkatesan, V., A survey on Multi Criteria Decision Making Methods and its applications (2013) Am J Inf Sys, 1 (1), pp. 31-43; Caris, A., Macharis, C., Janssens, G.K., Decision support in intermodal transport: A new research agenda (2013) Comput Ind, 64 (2), pp. 105-112; Cartenì, A., de Luca, S., Tactical and strategic planning for a container terminal: Modeling issues within a discrete event simulation approach (2012) Simulat Model Pract Theor, 21, pp. 23-45; Cavone, G., Dotoli, M., Seatzu, C., Management of intermodal freight terminals by First-Order Hybrid Petri Nets (2016) Rob Aut Lett, 1 (1), pp. 2-9; Cavone, G., Dotoli, M., Epicoco, N., Seatzu, C., Intermodal terminal planning by Petri Nets and Data Envelopment Analysis (2017) Control Eng Prac, 69, pp. 9-22; Charnes, A., Cooper, W.W., Rhodes, E., Measuring the efficiency of Decision Making Units (1978) Eur J Oper Res, 2, pp. 429-444; Di Febbraro, A., Sacco, N., Saeednia, M., An agent-based framework for cooperative planning of intermodal freight transport chains (2016) Transp Res C, 64, pp. 72-85; Dotoli, M., Epicoco, N., Falagario, M., Sciancalepore, F., A cross-efficiency fuzzy Data Envelopment Analysis technique for performance evaluation of decision making units under uncertainty (2015) Comput Ind Eng, 79, pp. 103-114; Dotoli, M., Epicoco, N., Falagario, M., Cavone, G., A Timed Petri Nets model for performance evaluation of intermodal freight transport terminals (2016) IEEE Trans Autom Sci Eng, 13 (2), pp. 842-857; http://www.ecotransit.org/calculation.en.html, Ecotransit, at; http://www.gtslogistic.com/en/integratedreport2016, GTS, at; Hangga, P., Shinoda, T., A Petri Net model and its simulation for straddle carrier direct-system operation in a container terminal (2017) Appl Mech Mater, 862, pp. 202-207; Li, L., Negenborn, R.R., De Schutter, B., Intermodal freight transport planning - A receding horizon control approach (2015) Transp Res C, 60, pp. 77-95; Maione, G., Mangini, A.M., Ottomanelli, M., A generalized stochastic Petri Net approach for modeling activities of human operators in intermodal container terminals (2016) IEEE Trans Autom Sci Eng, 13 (4), pp. 1504-1516; Silva, C.A., Guedes Soares, C., Signoret, J.P., Intermodal terminal cargo handling simulation using Petri Nets with predicates (2015) J Eng Marit Env, 229 (4), pp. 323-339; SteadieSeifi, M., Dellaert, N.P., Nuijten, W., Van Woensel, T., Raoufi, R., Multimodal freight transportation planning: A literature review (2014) Eur J Oper Res, 233, pp. 1-15; Velasquez, M., Hester, P.T., An analysis of Multi-Criteria Decision Making methods (2013) Int J Oper Res, 10 (2), pp. 56-66}, document_type={Conference Paper}, source={Scopus}, }`

- Cavone, G., Dotoli, M., Epicoco, N., Franceschelli, M. & Seatzu, C. (2018) Hybrid Petri Nets to Re-design Low-Automated Production Processes: the Case Study of a Sardinian Bakery , 265-270.

[Bibtex]`@CONFERENCE{Cavone2018265, author={Cavone, G. and Dotoli, M. and Epicoco, N. and Franceschelli, M. and Seatzu, C.}, title={Hybrid Petri Nets to Re-design Low-Automated Production Processes: the Case Study of a Sardinian Bakery}, year={2018}, volume={51}, number={7}, pages={265-270}, doi={10.1016/j.ifacol.2018.06.311}, note={cited By 13}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85050128384&doi=10.1016%2fj.ifacol.2018.06.311&partnerID=40&md5=832573ffac1f56c0f884bc55c52b23d1}, abstract={This paper shows the practical relevance of first-order hybrid Petri nets in the re-design process of low-automated production systems. In particular, we analyze the case study of a bakery producing “pane Carasau” a typical Sardinian bread, whose traditional production plant currently has difficulties in coping with the constant increase in market demand. Through first-order hybrid Petri nets, the current functioning and the operating features and dynamics of the case study are modelled, waste sources and bottlenecks are detected, and alternative re-designed scenarios are implemented and evaluated to identify the most suitable reengineering actions to be developed. © 2018}, author_keywords={First-Order Hybrid Petri nets; Production processes; Re-design; Reengineering}, keywords={Bakeries; Design; Reengineering, Automated production systems; Automated productions; Design process; First-order hybrid Petri nets; Hybrid Petri net; Market demand; Production plant; Production process, Petri nets}, references={Balduzzi, F., Giua, A., Menga, G., First-order hybrid Petri nets: a model for optimization and control (2015) IEEE Trans. Robot. Autom., 16 (4), pp. 382-399; Basile, F., Carbone, C., Chiacchio, P., Simulation and analysis of discrete-event control systems based on Petri nets using PNetLab (2007) Contr. Eng. Pract., 15 (2), pp. 241-259; Campos, J., Seatzu, C., Xie, X., (2014) Formal methods in manufacturing, , CRC Press; Cavone, G., Dotoli, M., Seatzu, C., Management of Intermodal Freight Terminals by First-Order Hybrid Petri Nets (2016) IEEE Robot. Autom. Lett., 1 (1), pp. 2-9; Choi, J.Y., Reveliotis, S.A., A generalized stochastic Petri net model for performance analysis and control of capacitated reentrant lines (2003) IEEE Trans. Robot. Autom., 19 (3), pp. 474-480; David, R., Alla, H., (2010) Discrete, continuous, and hybrid Petri nets, , Springer Science & Business Media; Dotoli, M., Epicoco, N., Falagario, M., Cavone, C., A Timed Petri Nets model for intermodal freight transport terminals. Proc. 12th IFAC/IEEE Workshop on Discrete Event Systems. In: B. Lennartson et al. (eds) (2014) Discrete Event Systems, 12, pp. 176-181; Dotoli, M., Epicoco, N., Falagario, M., Cavone, G., A Timed Petri Nets model for performance evaluation of intermodal freight transport terminals. (2016) IEEE Trans. Autom. Sci. Eng., 13 (2), pp. 842-857; Hu, H., Zhou, M.C., A Petri Net-based discrete-event control of automated manufacturing systems with assembly operations. (2015) IEEE Trans. Control Syst. Technol., 23 (2), pp. 513-534; Long, F., Zeiler, P., Bertsche, B., (2015) Potentials of coloured Petri nets for realistic availability modelling of production systems in industry 4.0. In Podofillini et al. (eds.), Safety and Reliability of Complex Engineered Systems, pp. 4455-4463. , CRC Press London; Negahban, A., Smith, J.S., Simulation for manufacturing system design and operation: Literature review and analysis. (2014) J. Manuf. Syst., 33, pp. 241-261; Pagani, M.A., Lucisano, M., Mariotti, M., (2014) Italian Bakery Products. In: W. Zhou et al. (eds.), Bakery Products Science and Technology, , John Wiley & Sons, Ltd 2nd ed; Paschino, F., Gambella, F., Giubellino, F., Clemente, F., The level of automation of “carasau” bread production plants. (2007) J. Agric. Eng., 2, pp. 61-64; Pawlewski, P., (2010), Using Petri Nets to model and simulation production systems in process reengineering (case study), In P. Pawlewski (ed.), Petri Nets Applications, InTech, 421-446; Sessego, F., Giua, A., Seatzu, C., (2008) HYPENS: A Matlab tool for timed discrete, continuous and hybrid Petri Nets, Applications and Theory of Petri Nets: Proc. 29th Int. Conf. on Applications and Theory of Petri nets. In: Lect. Notes Comput. Sci., pp. 419-428. , Springer-Verlag 5062; Silva, M., Recalde, L., On fluidification of Petri Nets: from discrete to hybrid and continuous models. (2004) Annual Reviews in Control, 28 (2), pp. 253-266}, document_type={Conference Paper}, source={Scopus}, }`

- Carli, R. & Dotoli, M. (2018) A Distributed Control Algorithm for Optimal Charging of Electric Vehicle Fleets with Congestion Management , 373-378.

[Bibtex]`@CONFERENCE{Carli2018373, author={Carli, R. and Dotoli, M.}, title={A Distributed Control Algorithm for Optimal Charging of Electric Vehicle Fleets with Congestion Management}, year={2018}, volume={51}, number={9}, pages={373-378}, doi={10.1016/j.ifacol.2018.07.061}, note={cited By 11}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85050093123&doi=10.1016%2fj.ifacol.2018.07.061&partnerID=40&md5=87f63aa0bd1f3aa33b573169b8a48e32}, abstract={This paper proposes a novel distributed control strategy for the optimal charging of a fleet of Electric Vehicles (EVs) in case of limited overall capacity of the electrical distribution network. The optimal charging is obtained as the solution of a scheduling problem aiming at a cost-optimal profile of the aggregated energy demand. The resulting optimization problem is formulated as a quadratic programming problem with a coupling of decision variables both in the objective function and in the constraint. We assume a minimal information structure, where users locally communicate only with their neighbors, without relying on a central decision maker. The solution approach relies on an iterative distributed algorithm based on duality, proximity, and consensus theory. A simulated case study demonstrates that the approach allows achieving the global optimum. © 2018}, author_keywords={Decentralized; Distributed Control; Electric Vehicles; Large scale optimization problems; Scheduling algorithms}, keywords={Charging (batteries); Decision making; Electric vehicles; Fleet operations; Iterative methods; Quadratic programming; Scheduling algorithms, Decentralized; Distributed control; Distributed control algorithms; Distributed control strategy; Electric Vehicles (EVs); Electrical distribution networks; Large-scale optimization; Quadratic programming problems, Traffic congestion}, references={Aravinthan, V., Jewell, W., Controlled electric vehicle charging for mitigating impacts on distribution assets (2015) IEEE Trans. Smart Grid, 6 (2), pp. 999-1009; Bertsekas, D.P., Tsitsiklis, J.N., (1989) Parallel and distributed computation: numerical methods, , Prentice hall Englewood Cliffs, NJ (Vol. 23); Boyd, S., Vandenberghe, L., (2004) Convex Optimization, , Cambridge University Press UK 2004; Carli, R., Dotoli, M., (2014), pp. 5648-5653. , Energy Scheduling of a Smart Home under Nonlinear Pricing, Proc. IEEE CDC; Carli, R., Dotoli, M., Pellegrino, R., A Hierarchical Decision Making Strategy for the Energy Management of Smart Cities (2017) IEEE Trans. Aut. Sci. Eng., 14 (2), pp. 505-523; Carli, R., Dotoli, M., A Distributed Control Algorithm for Waterfilling of Networked Control Systems via Consensus (2017) IEEE Control Systems Letters, 1 (2), pp. 334-339; Carli, R., Dotoli, M., (2017), Decentralized Optimal Charging Control of Electric Vehicle Fleets with Congestion Management Proc. IEEE SOLI, Bari, Italy; de Hoog, J., Alpcan, T., Brazil, M., Thomas, D.A., Mareels, I., Optimal charging of electric vehicles taking distribution network constraints into account (2015) IEEE Trans. Power Sys., 30 (1), pp. 365-375; Falsone, A., Margellos, K., Garatti, S., Prandini, M., Distributed constrained convex optimization and consensus via dual decomposition and proximal minimization (2016) Proc. IEEE CDC, pp. 1889-1894; Garin, F., Schenato, L., (2011), pp. 75-107. , Networked Control Systems. Springer, ch. A Survey on distributed estimation and control applications using linear consensus algorithms; Gan, L., Topcu, U., Low, S., „Optimal decentralized protocol for electric vehicle charging‟ (2013) IEEE Trans. Power Syst., 28 (2), pp. 940-951; Grammatico, S., Parise, F., Colombino, M., Lygeros, J., Decentralized convergence to Nash equilibria in constrained mean field control (2016) IEEE Trans. Aut. Contr, 61 (11), pp. 3315-3329; Grammatico, S., Dynamic Control of Agents Playing Aggregative Games With Coupling Constraints (2017) IEEE Trans. Aut. Contr, 62 (9), pp. 4537-4548; He, Y., Venkatesh, B., Guan, L., Optimal scheduling for charging and discharging of electric vehicles (2012) IEEE Trans. Smart Grid, 3 (3), pp. 1095-1105; Le Floch, C., Belletti, F., Moura, S., Optimal Charging of Electric Vehicles for Load Shaping: A Dual-Splitting Framework With Explicit Convergence Bounds (2016) IEEE Trans. Transp. Electr., 2 (2), pp. 190-199; Ma, Z., Callaway, D.S., Hiskens, I.A., Decentralized charging control of large populations of plug-in electric vehicles (2013) IEEE Trans. Control Syst. Technol., 21 (1), pp. 67-78; Ma, W.J., Gupta, V., Topcu, U., On distributed charging control of electric vehicles with power network capacity constraints (2014) Proc. IEEE ACC, pp. 4306-4311; Rivera, J., Wolfrum, P., Hirche, S., Goebel, C., Jacobsen, H.A., Alternating direction method of multipliers for decentralized electric vehicle charging control (2013) Proc. IEEE CDC, pp. 6960-6965; Shaaban, M.F., Atwa, Y.M., El-Saadany, E.F., PEVs modeling and impacts mitigation in distribution networks (2013) IEEE Trans. Power Syst., 28 (2), pp. 1122-1131; Sortomme, E., Hindi, M.M., MacPherson, S.D.J., Venkata, S.S., Coordinated charging of plug-in hybrid electric vehicles to minimize distribution system losses (2011) IEEE Trans. Smart Grid, 2 (1), pp. 198-205; Stüdli, S., Crisostomi, E., Middleton, R., Shorten, R., A flexible distributed framework for realising electric and plug-in hybrid vehicle charging policies (2012) Int. J. Control, 85 (8), pp. 1130-1145; Su, W., Rahimi-Eichi, H., Zeng, W., Chow, M.Y., A survey on the electrification of transportation in a smart grid environment (2012) IEEE Trans. Ind. Inf., 8, pp. 1-10; Sun, B., Huang, Z., Tan, X., Tsang, D.H.K., Optimal Scheduling for Electric Vehicle Charging with Discrete Charging Levels in Distribution Grid (2016) IEEE Trans. Smart Grid, 99, p. 1; Wen, C.K., Chen, J.C., Teng, J.H., Ting, P., Decentralized plug-in electric vehicle charging selection algorithm in power systems (2012) IEEE Trans. on Smart Grid, 3 (4), pp. 1779-1789; Xu, Z., Hu, Z., Song, Y., Zhao, W., Zhang, Y., Coordination of PEVs charging across multiple aggregators (2014) Appl. Ener., 136, pp. 582-589}, document_type={Conference Paper}, source={Scopus}, }`

### 2017

- Cavone, G., Dotoli, M., Epicoco, N. & Seatzu, C. (2017) A decision making procedure for robust train rescheduling based on mixed integer linear programming and Data Envelopment Analysis. IN Applied Mathematical Modelling, 52.255-273.

[Bibtex]`@ARTICLE{Cavone2017255, author={Cavone, G. and Dotoli, M. and Epicoco, N. and Seatzu, C.}, title={A decision making procedure for robust train rescheduling based on mixed integer linear programming and Data Envelopment Analysis}, journal={Applied Mathematical Modelling}, year={2017}, volume={52}, pages={255-273}, doi={10.1016/j.apm.2017.07.030}, note={cited By 20}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85032351265&doi=10.1016%2fj.apm.2017.07.030&partnerID=40&md5=9878b46aea5aa585ef08d87ee28e4b38}, abstract={This paper presents a self-learning decision making procedure for robust real-time train rescheduling in case of disturbances. The procedure is applicable to aperiodic timetables of mixed-tracked networks and it consists of three steps. The first two are executed in real-time and provide the rescheduled timetable, while the third one is executed offline and guarantees the self-learning part of the method. In particular, in the first step, a robust timetable is determined, which is valid for a finite time horizon. This robust timetable is obtained solving a mixed integer linear programming problem aimed at finding the optimal compromise between two objectives: the minimization of the delays of the trains and the maximization of the robustness of the timetable. In the second step, a merging procedure is first used to join the obtained timetable with the nominal one. Then, a heuristics is applied to identify and solve all conflicts eventually arising after the merging procedure. Finally, in the third step an offline cross-efficiency fuzzy Data Envelopment Analysis technique is applied to evaluate the efficiency of the rescheduled timetable in terms of delays minimization and robustness maximization when different relevance weights (defining the compromise between the two optimization objectives) are used in the first step. The procedure is thus able to determine appropriate relevance weights to employ when disturbances of the same type affect again the network. The railway service provider can take advantage of this procedure to automate, optimize, and expedite the rescheduling process. Moreover, thanks to the self-learning capability of the procedure, the quality of the rescheduling is improved at each reapplication of the method. The technique is applied to a real data set related to a regional railway network in Southern Italy to test its effectiveness. © 2017 Elsevier Inc.}, author_keywords={Data Envelopment Analysis; Decision making; Railways; Real-time; Rescheduling; Robustness}, keywords={Data envelopment analysis; Decision making; Efficiency; Merging; Optimization; Railroad transportation; Railroads; Robustness (control systems); Scheduling; Statistical tests; Transportation, Decision making procedure; Fuzzy data envelopment analysis; Mixed integer linear programming; Mixed integer linear programming problems; Railways; Real time; Rescheduling; Self-learning capability, Integer programming}, references={Castillo, E., Gallego, I., Urena, J.M., Coronado, J.M., Timetabling optimization of a mixed double- and single-tracked railway network (2011) Appl. Math. Model., 35, pp. 859-878; Dotoli, M., Epicoco, N., Falagario, M., Piconese, A., Sciancalepore, F., Turchiano, B., A real-time traffic management model for regional railway network under disturbances (2013) Proceedings of the Ninth International Conference on Automation Science and Engineering, pp. 892-897; Guo, X., Wu, J., Sun, H., Liu, R., Gao, Z., Timetable coordination of first trains in urban railway network: a case study of Beijing (2016) Appl. Math. Model., 40, pp. 8048-8066; Hassannayebi, E., Zegordi, S.H., Yaghini, M., Train timetabling for an urban rail transit line using a Lagrangian relaxation approach (2016) Appl. Math. Model., 40, pp. 9892-9913; Van Aken, S., Bešinović, N., Goverde, R.M., Designing alternative railway timetables under infrastructure maintenance possessions (2017) Transport. Res. B Methodol., 98, pp. 224-238; Li, X., Shou, B., Ralescu, D., Train rescheduling with stochastic recovery time: a new track-backup approach (2014) IEEE Trans. Syst. Man Cybern. Syst., 44 (9), pp. 1216-1233; Cacchiani, V., Huisman, D., Kidd, M., Kroon, L., Toth, P., Veelenturf, L., Wagenaar, J., An Overview of Recovery Models and Algorithms For Real-Time Railway Rescheduling (2013), Econometric Institute Report EI2013-29; Hassannayebi, E., Sajedinejad, A., Mardani, S., Disruption management in urban rail transit system: a simulation based optimization approach (2016) Handbook of Research on Emerging Innovations in Rail Transportation Engineering, pp. 420-450. , IGI Global; Dollevoet, T., Huisman, D., Kroon, L.G., Veelenturf, L.P., Wagenaar, J.C., Application of an iterative framework for real-time railway rescheduling (2017) Comput. Oper. Res., 78, pp. 203-217; Dotoli, M., Epicoco, N., Falagario, M., Turchiano, B., Cavone, G., Convertini, A., A decision support system for real-time rescheduling of railways (2014) Proc. Eur. Control Conf., pp. 696-701; Dotoli, M., Epicoco, N., Falagario, M., Sciancalepore, F., A cross-efficiency fuzzy Data Envelopment Analysis technique for performance evaluation of decision making units under uncertainty (2015) Comput. Ind. Eng., 79, pp. 103-114; Törnquist, J., Computer-based decision support for railway traffic scheduling and dispatching: a review of models and algorithms (2006) Proceedings of the Fifth Workshop on Algorithm Methods Models for Optimization Railways; D'Ariano, A., Innovative decision support system for railway traffic control (2009) Intell. Transp. Syst. Mag., 1, pp. 8-16; Corman, F., Meng, L., A review of online dynamic models and algorithms for railway traffic management (2015) IEEE Trans. Intell. Transp. Syst., 16 (3), pp. 1274-1284; Fang, W., Yang, S., Yao, X., A survey on problem models and solution approaches to rescheduling in railway networks (2015) IEEE Trans. Intell. Transp. Syst., 16 (6), pp. 2997-3016; Xu, X., Li, K., Yang, L., Ye., J., Balanced train timetabling on a single-line railway with optimized velocity (2014) Appl. Math. Model., 38, pp. 894-909; Gao, Y., Yang, L., Li., S., Uncertain models on railway transportation planning problem (2016) Appl. Math. Model., 40, pp. 4921-4934; Andersson, E.V., Peterson, A., Törnquist, J.K., Quantifying railway timetable robustness in critical points (2013) J. Rail Transp. Plan. Manag., 3 (3), pp. 95-110; Salido, M.A., Barber, F., Ingolotti, L., Robustness for a single railway line: analytical and simulation methods (2012) Exp. Syst. Appl., 39, pp. 13305-13327; Hassannayebi, E., Zegordi, S.H., Amin-Naseri, M.R., Yaghini, M., Train timetabling at rapid rail transit lines: a robust multi-objective stochastic programming approach (2017) Oper. Res., 17 (2), pp. 435-477; Dewilde, T., Sels, P., Cattrysse, D., Vansteenwegen, P., Defining robustness of a railway timetable (2011) Proceedings of the Fourth International Seminar on Railway Operational Modelling and Analysis, pp. 1-20. , RailRome; Kroon, L.G., Huisman, D., Maróti, G., Optimisation models for railway timetabling (2008) Railway Timetable and Traffic, , I. Hansen J. Pachl Eurailpress Hamburg; D'Ariano, A., Pacciarelli, D., Pranzo, M., Assessment of flexible timetables in real-time traffic management of a railway bottleneck (2008) Transp. Res. C Emer., 16 (2), pp. 232-245; Kroon, L.G., Dekker, R., Vromans, M.J., Cyclic Railway timetabling: A stochastic Optimization Approach (2007), pp. 41-66. , Springer Berlin Heidelberg; Salido, M.A., Barber, F., Ingolotti, L., Robustness in railway transportation scheduling (2008) Proceedings of the Intelligent Control Automation Conference, pp. 2880-2885; Fischetti, M., Salvagnin, D., Zanette, A., Fast approaches to improve the robustness of a railway timetable (2009) Transp. Sci., 43 (3), pp. 321-335; Liebchen, C., Schachtebeck, M., Schoebel, A., Stiller, S., Prigge, A., Computing delay resistant railway timetables (2010) Comput. Oper. Res., 37 (5), pp. 857-868; Larsen, R., Pranzo, M., D'Ariano, A., Corman, F., Pacciarelli, D., Susceptibility of optimal train schedules to stochastic disturbances of process times (2014) Flex. Serv. Manuf. J., 26 (4), pp. 466-489; Jovanović, P., Kecman, P., Bojović, N., Mandić, D., Optimal allocation of buffer times to increase train schedule robustness (2017) Eur. J. Oper. Res., 256 (1), pp. 44-54; Velasquez, M., Hester, P.T., An analysis of multi-criteria decision making methods (2013) Int. J. Oper. Res., 10 (2), pp. 56-66; Markovits-Somogy, R., Ranking efficient and inefficient decision making units in Data Envelopment Analysis (2011) Int. J. Traffic Transp. Eng., 1 (4), pp. 245-256; Törnquist, J., Persson, A., N-tracked railway traffic re-scheduling during disturbances (2007) Transp. Res. B, 41, pp. 342-362; Adenso-Díaz, B., González, M.O., González-Torre, P., On-line timetable re-scheduling in regional train services (1999) Transp. Res. B, 33 (6), pp. 387-398; Samà, M., Meloni, C., D'Ariano, A., Corman, F., A multi-criteria decision support methodology for real-time train scheduling (2015) J. Rail Transp. Plan. Manag., 5 (3), pp. 146-162; Oneto, L., Fumeo, E., Clerico, G., Canepa, R., Papa, F., Dambra, C., Mazzino, N., Anguita, D., Dynamic delay predictions for large-scale railway networks: deep and shallow extreme learning machines tuned via thresholdout (2017) IEEE Trans. Syst. Man Cybern. Syst., , In press; Hu, X., Cui, N., Demeulemeester, E., Bie, L., Incorporation of activity sensitivity measures into buffer management to manage project schedule risk (2016) Eur. J. Oper. Res., 249 (2), pp. 717-727; Şahin, I., Railway traffic control and train scheduling based on inter-train conflict management (1999) Transp. Res. B, 33, pp. 511-534; Vromans, M.J., Dekker, R., Kroon, L.G., Reliability and heterogeneity of railway services (2006) Eur. J. Oper. Res., 172, pp. 647-665; Zimmermann, H.-J., Fuzzy Set Theory and its Applications (2001), fourth ed. Springer Science & Business Media New York; Jimenez, M., Bilbao, A., Pareto-optimal solution in fuzzy multi-objective linear programming (2009) Fuzzy Sets Syst., 160, pp. 2714-2721}, document_type={Article}, source={Scopus}, }`

- Cavone, G., Dotoli, M., Epicoco, N. & Seatzu, C. (2017) Intermodal terminal planning by Petri Nets and Data Envelopment Analysis. IN Control Engineering Practice, 69.9-22.

[Bibtex]`@ARTICLE{Cavone20179, author={Cavone, G. and Dotoli, M. and Epicoco, N. and Seatzu, C.}, title={Intermodal terminal planning by Petri Nets and Data Envelopment Analysis}, journal={Control Engineering Practice}, year={2017}, volume={69}, pages={9-22}, doi={10.1016/j.conengprac.2017.08.007}, note={cited By 13}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85028873437&doi=10.1016%2fj.conengprac.2017.08.007&partnerID=40&md5=f1dac97345f97f58a4fee381c92037d4}, abstract={A procedure for planning and resources’ management in intermodal terminals is presented. It integrates Timed Petri Nets (TPNs) and Data Envelopment Analysis (DEA) and consists of three steps: the terminal modeling via TPNs to model the regular behavior; the evaluation of whether the current configuration may cope with increased freight flows; if not, the analysis by cross-efficiency DEA of alternative planning solutions. The procedure provides the decision maker with number, capacity, and schedule of resources to tackle the flows increase. The method is evaluated by a real case study, showing that integrating TPNs and DEA allows taking planning decisions under conflicting requirements. © 2017}, author_keywords={Data Envelopment Analysis; Freight transportation; Intermodal terminals; Performance evaluation; Petri Nets; Resource planning}, keywords={Data envelopment analysis; Decision making; Freight transportation; Petri nets, Cross efficiency; Current configuration; Decision makers; Intermodal terminals; Performance evaluation; Resource planning; Terminal model; Timed Petri Net, Intermodal transportation}, references={Alicke, K., Modelling and optimization of the intermodal terminal Mega Hub (2002) OR Spectrum, 24, pp. 1-17; Angulo Meza, L., Pereira Estellita Lins, M., Review of methods for increasing discrimination in Data Envelopment Analysis (2002) Annals of Operations Research, 116 (1-4), pp. 225-242; Araz, C., Ozkarahan, I., Supplier evaluation and management system for strategic sourcing based on a new multicriteria sorting procedure (2007) International Journal of Production Economics, 106, pp. 585-606; Basile, F., Carbone, C., Chiacchio, P., Simulation and analysis of discrete-event control systems based on Petri nets using PNetLab (2007) Control Engineering Practice, 15 (2), pp. 241-259; Basile, F., Chiacchio, P., Teta, D., A hybrid model for real time simulation of urban traffic (2012) Control Engineering Practice, 20 (2), pp. 123-137; Bontekoning, Y.M., Macharis, C., Trip, J.J., Is a new applied transportation research field emerging? A review of intermodal rail-truck freight transport literature (2008) Transportation Research, 38, pp. 1-34; Boschian, V., Dotoli, M., Fanti, M.P., Iacobellis, G., Ukovich, W., A metamodelling approach to the management of intermodal transportation networks (2011) IEEE Transactions on Automation Science and Engineering, 8 (3), pp. 457-469; Caris, A., Macharis, C., Janssens, G.K., Planning problems in intermodal freight transport: Accomplishments and prospects (2008) Transport Plan Technology, 31 (3), pp. 277-302; Caris, A., Macharis, C., Janssens, G.K., Decision support in intermodal transport: A new research agenda (2013) Computers & Industrial, 64 (2), pp. 105-112; Cartenì, A., de Luca, S., Tactical and strategic planning for a container terminal: Modeling issues within a discrete event simulation approach (2012) Simulation Modelling Practice and Theory, 21, pp. 23-45; Cavone, G., Dotoli, M., Seatzu, C., Management of intermodal freight terminals by First-Order Hybrid Petri Nets (2016) Robotics and Letters, 1 (1), pp. 2-9; Cavone, G., Dotoli, M., Seatzu, C., (2016b). Resource planning of intermodal terminals using Timed Petri Nets. In Proc 13th Int Workshop on Discrete Event Systems, Xi'an, China, May 30–June 1; Charnes, A., Cooper, W.W., Rhodes, E., Measuring the efficiency of Decision Making Units (1978) European Journal of Operational Research, 2, pp. 429-444; Chen, H., Amodeo, L., Feng, C., Labadi, K., Modelling and performance evaluation of Supply Chains using batch deterministic and Stochastic Petri Nets (2005) IEEE Transactions on Automation Science and Engineering, 2 (2), pp. 132-144; David, R., Alla, H., (2005) Discrete, Continuous, and Hybrid Petri Nets, , Springer-Verlag Berlin Heidelberg; Degano, C., Di Febbraro, A., Modelling automated material handling in intermodal terminals (2001), In Proc Int Conf Advanced Intelligent Mechatronics, Como; Degano, C., Di Febbraro, A., (2002), On using Petri Nets to detect faulty behaviours in an intermodal container terminal. In Proc Intermodality, Sustainability and Intelligent Transportation Systems; Degano, C., Pellegrino, A., (2002), Multi-agent coordination and collaboration for control and optimization strategies in an intermodal container terminal. In Proc Int Conf Engin Manag; Di Febbraro, A., Porta, G., & Sacco, N. (2006). A Petri Net modelling approach of intermodal terminals based on Metrocargo system. In Proc IEEE Int Conf Intell Transp Sys Conf, Toronto; Di Febbraro, A., Sacco, N., On modelling urban transportation networks via hybrid Petri nets (2004) Control Engineering Practice, 12 (10), pp. 1225-1239; Dotoli, M., Epicoco, N., Falagario, M., Cavone, G., A Timed Petri Nets model for performance evaluation of intermodal freight transport terminals (2016) IEEE Transactions on Automation Science and Engineering, 13 (2), pp. 842-857; Dotoli, M., Epicoco, N., Falagario, M., Sciancalepore, F., A cross-efficiency fuzzy Data Envelopment Analysis technique for performance evaluation of decision making units under uncertainty (2015) Computers & Industrial Engineering, 79, pp. 103-114; Dotoli, M., Epicoco, N., Falagario, M., Sciancalepore, F., A stochastic cross-efficiency Data Envelopment Analysis approach for supplier selection under uncertainty (2016) International Transactions in Operational Research, 23 (4), pp. 725-748; Dotoli, M., Epicoco, N., Falagario, M., Seatzu, C., Turchiano, B., A Decision Support System for optimizing operations at intermodal railroad terminals (2016) IEEE Transactions on Systems, Man, and Cybernetics, 47 (3), pp. 487-501; Dotoli, M., Fanti, M.P., A Coloured Petri Net model for automated storage and retrieval systems serviced by rail-guided vehicles: A control perspective (2005) International Journal Computer Integral Manufacturing, 18 (2-3), pp. 122-136; Dotoli, M., Fanti, M.P., Iacobellis, G., Mangini, A.M., A first order Hybrid Petri Net model for Supply Chain Management (2009) IEEE Transactions on Automation Science and Engineering, 6 (4), pp. 744-758; Dotoli, M., Fanti, M.P., Mangini, A.M., Stecco, G., Ukovich, W., The impact of ICT on intermodal transportation systems: A modelling approach by Petri nets (2010) Control Engineering Practice, 18 (8), pp. 893-903; Doyle, J.R., Green, R.H., Cross-Evaluation in DEA: Improving Discrimination Among DMUs (1995) Information Systems Operations Research, 33 (3), pp. 205-222; http://www.ecotransit.org/calculation.en.html, Ecotransit, available at; Fanti, M.P., Giua, A., Seatzu, C., Monitor design for colored Petri nets: An application to deadlock prevention in railway networks (2006) Control Engineering Practice, 14 (10), pp. 1231-1247; Filipova, K., Stojadinova, T., Hadjiatanasova, V., Application of Petri Nets for transport streams modelling (2002) Facta Universitatis: Architecture and Civil Engineering, 2 (4), pp. 295-306; Giua, A., Seatzu, C., Modelling and supervisory control of railway networks using Petri Nets (2008) IEEE Transactions on Automation Science and Engineering, 5 (3), pp. 431-445; http://www.gtstrasporti.com/en/annual-report-2014, GTS, Annual Report 2014, (2014), available at; Günther, H.O., Kim, K.H., Container terminals and terminal operations (2006) OR Spectrum, 28, pp. 437-445; Hammadi, S., Ksouri, M., (2013) Multimodal Transport Systems, , John Wiley & Sons; Hangga, P., Shinoda, T., A Petri Net model and its simulation for straddle carrier direct-system operation in a container terminal (2017) Applied Mechanics & Materials, 862, pp. 202-207; Harris, I., Wang, Y.L., Wang, H.Y., ICT in multimodal transport and technological trends: Unleashing potential for the future (2015) International Journal of Production Economics, 159, pp. 88-103; Khalili, M., Camanho, A.S., Portela, M.C.A.S., Alirezaee, M.R., The measurement of relative efficiency using Data Envelopment Analysis with Assurance Regions that link inputs and outputs (2010) European Journal of Operational Research, 203, pp. 761-770; Law, A.L., (2007) Simulation Modelling & Analysis, , New York: McGraw Hill; Liu, C.I., Ioannou, P.A., Petri Net modelling and analysis of automated container terminal using Automated Guided Vehicle systems (2002) Transportation Research Record, 1782, pp. 73-83; Maione, G., Mangini, A.M., Ottomanelli, M., A generalized Stochastic Petri Net approach for modeling activities of human operators in intermodal container terminals (2016) IEEE Transactions on Automation Science and Engineering, 13 (4), pp. 1504-1516; Maione, G., Ottomanelli, M., (2005), A preliminary Petri Net model of the transshipment processes in the Taranto container terminal. In Int Conf Emerging Technologies and Factory Automation, Catania; Markovits-Somogy, R., Ranking efficient and inefficient Decision Making Units in Data Envelopment Analysis (2011) Internationa Journal Traffic Transportation Engineering, 1 (4), pp. 245-256; Murty, K.G., Liu, J., Wan, Y.W., Linn, R., A Decision Support System for operations in a container terminal (2005) Decision Support Systems, 39 (3), pp. 309-332; Sessego, F., Giua, A., Seatzu, C., (2008) HYPENS: A Matlab Tool for Timed Discrete, Continuous and Hybrid Petri Nets, LNCS, 5062, pp. 419-428. , Springer-Verlag; Sexton, T.R., Silkman, R.H., Hogan, A.J., Data Envelopment Analysis: Critique and extensions (1986) Measuring Efficiency: An Assessment of Data Envelopment Analysis, , Silkman R.H. CA: Jossey-Bass San Francisco; Silva, C.A., Guedes Soares, C., Signoret, J.P., Intermodal terminal cargo handling simulation using Petri Nets with predicates (2015) Journal Engineering Maritime Environment, 229 (4), pp. 323-339; Stahlbock, R., Voss, S., Operations research at container terminals: A literature update (2008) OR Spectrum, 30, pp. 1-52; Steenken, D., Voß, S., Stahlbock, R., Container terminal operation and operations research - A classification and literature review (2004) OR Spectrum, 26, pp. 3-49; http://www.cargo.trenitalia.it/cargo_en.html, Trenitalia, available at; Velasquez, M., Hester, P.T., An analysis of Multi-Criteria Decision Making methods (2013) International Journal of Operations Research, 10 (2), pp. 56-66; Wang, J., (1998) Timed Petri Nets: Theory and Application, , Springer; Zimmermann, A., (2008) Stochastic Discrete Event Systems: Modelling, Evaluation, Applications, , Springer}, document_type={Article}, source={Scopus}, }`

- Carli, R. & Dotoli, M. (2017) A decentralized control strategy for optimal charging of electric vehicle fleets with congestion management IN Proceedings – 2017 IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI 2017., 63-67.

[Bibtex]`@CONFERENCE{Carli201763, author={Carli, R. and Dotoli, M.}, title={A decentralized control strategy for optimal charging of electric vehicle fleets with congestion management}, journal={Proceedings - 2017 IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI 2017}, year={2017}, volume={2017-January}, pages={63-67}, doi={10.1109/SOLI.2017.8120971}, note={cited By 9}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85046260212&doi=10.1109%2fSOLI.2017.8120971&partnerID=40&md5=2ced08ace41770b457c6f07d8cbe98b5}, abstract={This paper proposes a novel decentralized control strategy for the optimal charging of a large-scale fleet of Electric Vehicles (EVs). The scheduling problem aims at ensuring a cost-optimal profile of the aggregated energy demand and at satisfying the resource constraints depending both on power grid components capacity and EV locations in the distribution network. The resulting optimization problem is formulated as a quadratic programming problem with a coupling of decision variables both in the objective function and in the inequality constraints. The solution approach relies on a decentralized optimization algorithm that is based on a variant of ADMM (Alternating Direction Method of Multipliers), adapted to take into account the inequality constraints and the non-separated objective function. A simulated case study demonstrates that the approach allows achieving both the overall fleet and individual EV goals, while complying with the power grid congestion limits. © 2017 IEEE.}, author_keywords={Alternating direction method of multipliers; Congestion management; Coupled objective function; Decentralized optimization; Electric vehicle charging; Sharing}, keywords={Charging (batteries); Constraint satisfaction problems; Constraint theory; Decentralized control; Electric power transmission networks; Electric vehicles; Fleet operations; Quadratic programming; Traffic congestion, Alternating direction method of multipliers; Congestion management; Decentralized optimization; Electric vehicle charging; Objective functions; Sharing, Electric machine control}, references={Su, W., Rahimi-Eichi, H., Zeng, W., Chow, M.-Y., A survey on the electrification of transportation in a smart grid environment (2012) IEEE Trans. Ind. Inf., 8, pp. 1-10; Carli, R., Dotoli, M., Pellegrino, R., A hierarchical decision making strategy for the energy management of smart cities (2016) IEEE Trans. Aut. Sci. Eng.; Simpson, A., (2006) Cost-benefit Analysis of Plug-in Hybrid Electric Vehicle Technology, , Golden: National Renewable Energy Laboratory; Ma, Z., Callaway, D.S., Hiskens, I.A., Decentralized charging control of large populations of plug-in electric vehicles (2013) IEEE Transactions on Control Systems Technology, 21 (1), pp. 67-78; Gan, L., Topcu, U., Low, S., Optimal decentralized protocol for electric vehicle charging (2013) IEEE Trans. Power Syst., 28 (2), pp. 940-951; Sortomme, E., Hindi, M.M., MacPherson, S.D.J., Venkata, S.S., Coordinated charging of plug-in hybrid electric vehicles to minimize distribution system losses (2011) IEEE Trans. Smart Grid, 2 (1), pp. 198-205; Carli, R., Dotoli, M., A distributed control algorithm for waterfilling of networked control systems via consensus IEEE Control Systems Letters, PP (99), p. 1; Rivera, J., Wolfrum, P., Hirche, S., Goebel, C., Jacobsen, H.A., Alternating direction method of multipliers for decentralized electric vehicle charging control (2013) Proc. 52nd IEEE Conf. Decision and Control, pp. 6960-6965; Le Floch, C., Belletti, F., Saxena, S., Bayen, A.M., Moura, S., Distributed optimal charging of electric vehicles for demand response and load shaping (2015) 2015 54th IEEE Conference on Decision and Control (CDC), pp. 6570-6576. , Osaka; Grammatico, S., Parise, F., Colombino, M., Lygeros, J., Decentralized convergence to Nash equilibria in constrained mean field control (2014) IEEE Trans. Aut. Contr., 61 (11), pp. 3315-3329. , Nov; Aravinthan, V., Jewell, W., Controlled electric vehicle charging for mitigating impacts on distribution assets (2015) IEEE Trans. Smart Grid, 6 (2), pp. 999-1009; De Hoog, J., Alpcan, T., Brazil, M., Thomas, D.A., Mareels, I., Optimal charging of electric vehicles taking distribution network constraints into account (2015) IEEE Trans. Power Sys., 30 (1), pp. 365-375; Xu, Z., Hu, Z., Song, Y., Zhao, W., Zhang, Y., Coordination of PEVs charging across multiple aggregators (2014) Appl. Ener., 136, pp. 582-589; Grammatico, S., (2016) Dynamic Control of Agents Playing Aggregative Games, , arXiv preprint; Ma, W.J., Gupta, V., Topcu, U., On distributed charging control of electric vehicles with power network capacity constraints (2014) 2014 American Control Conference, pp. 4306-4311. , June IEEE; Le Floch, C., Belletti, F., Moura, S., Optimal charging of electric vehicles for load shaping: A dual-splitting framework with explicit convergence bounds (2016) IEEE Trans. Transp. Electr., 2 (2), pp. 190-199; Carli, R., Dotoli, M., Energy scheduling of a smart home under nonlinear pricing (2014) 53th IEEE Conference on Decision and Control (CDC), 6p; Boyd, S., Vandenberghe, L., (2004) Convex Optimization, , Cambridge University Press, UK; Boyd, S., Parikh, N., Chu, E., Peleato, B., Eckstein, J., Distributed optimization and statistical learning via the alternating direction method of multipliers (2010) Found. Trends Mach. Learn., 3 (1), pp. 1-122; Deng, W., (2013) Parallel Multi-block ADMM with O (1/k) Convergence, , arXiv preprint}, document_type={Conference Paper}, source={Scopus}, }`

- Carli, R. & Dotoli, M. (2017) A distributed control algorithm for waterfilling of networked control systems via consensus. IN IEEE Control Systems Letters, 1.334-339.

[Bibtex]`@ARTICLE{Carli2017334, author={Carli, R. and Dotoli, M.}, title={A distributed control algorithm for waterfilling of networked control systems via consensus}, journal={IEEE Control Systems Letters}, year={2017}, volume={1}, number={2}, pages={334-339}, doi={10.1109/LCSYS.2017.2716190}, note={cited By 14}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85050079250&doi=10.1109%2fLCSYS.2017.2716190&partnerID=40&md5=595d4be8879ccf3a42f0de75793d9038}, abstract={This letter presents a distributed waterfilling algorithm for networked control systems where users communicate with neighbors only. Waterfilling—a well-known optimization approach in communication systems—has inspired practical resolution methods for several control engineering and decision-making problems. This letter proposes a fully distributed solution for waterfilling of networked control systems. We consider multiple coupled waterlevels among users that locally communicate only with neighbors, without a central decision maker. We define two alternative versions (an exact one and an approximated one) of a novel distributed algorithm combining consensus, proximity, and the fixed point mapping theory, and show its convergence. We illustrate the technique by a case study on the charging of a fleet of electric vehicles. © 2017 IEEE.}, author_keywords={Distributed control; Networked control systems; Optimization}, keywords={Control theory; Decision making; Distributed parameter control systems; Fleet operations; Optimization, Decision makers; Decision-making problem; Distributed control; Distributed control algorithms; Distributed solutions; Optimization approach; Resolution methods; Water-filling algorithm, Networked control systems}, references={Boyd, S., Vandenberghe, L., (2004) Convex Optimization, , Cambridge, U.K.: Cambridge Univ. Press; Stavrou, P.A., Charalambous, T., Charalambous, C.D., Filtering with fidelity for time-varying Gauss-Markov processes (2016) Proc. IEEE CDC, pp. 5465-5470. , Dec; Fang, S., Ishii, H., Chen, J., Trade-offs in information-limited feedback systems: MIMO Bode-type integrals and power allocation (2015) Proc. IEEE CDC, pp. 6178-6183. , Dec; Zhou, X.S., Rui, Y., Huang, T.S., Water-filling: A novel way for image structural feature extraction (1999) Proc. IEEE Int. Conf. Image Process. (ICIP), pp. 570-574. , Kobe, Japan; Palomar, D.P., Fonollosa, J.R., Practical algorithms for a family of waterfilling solutions (2005) IEEE Trans. Signal Process., 53 (2), pp. 686-695. , Feb; Scutari, G., Palomar, D.P., Barbarossa, S., Optimal linear precoding strategies for wideband non-cooperative systems based on game theory—Part II: Algorithms (2008) IEEE Trans. Signal Process., 56 (3), pp. 1250-1267. , Mar; Mou, Y., Xing, H., Lin, Z., Fu, M., Decentralized optimal demand-side management for PHEV charging in a smart grid (2015) IEEE Trans. Smart Grid, 6 (2), pp. 726-736. , Mar; He, P., Li, M., Zhao, L., Venkatesh, B., Li, H., Water-filling exact solutions for load balancing of smart power grid systems IEEE Trans. Smart Grid, , to be published; Berinde, V., (2007) Iterative Approximation of Fixed Points, , Heidelberg, Germany: Springer; Carli, R., Dotoli, M., A decentralized resource allocation approach for sharing renewable energy among interconnected smart homes (2015) Proc. IEEE CDC, pp. 5903-5908. , Dec; Parikh, N., Boyd, S., Proximal algorithms (2014) Found. Trends Optim., 1 (3), pp. 127-147; Facchinei, F., Pang, J.-S., (2003) Finite-Dimensional Variational Inequalities and Complementarity Problem, , New York, NY, USA: Springer-Verlag; Olfati-Saber, R., Fax, J.A., Murray, R.M., Consensus and cooperation in networked multi-agent systems (2007) Proc. IEEE, 95 (1), pp. 215-233. , Jan; Le Floch, C., Belletti, F., Saxena, S., Bayen, A.M., Moura, S., Distributed optimal charging of electric vehicles for demand response and load shaping (2015) Proc. IEEE CDC, pp. 6570-6576. , Osaka, Japan; Garin, F., Schenato, L., A survey on distributed estimation and control applications using linear consensus algorithms (2010) Networked Control Systems, pp. 75-107. , London, U.K.: Springer; Tsianos, K., Lawlor, S., Rabbat, M.G., Communication/computation tradeoffs in consensus-based distributed optimization (2012) Proc. Adv. Neural Inf. Process. Syst., pp. 1943-1951}, document_type={Article}, source={Scopus}, }`

- Dotoli, M., Zgaya, H., Russo, C. & Hammadi, S. (2017) A Multi-Agent Advanced Traveler Information System for Optimal Trip Planning in a Co-Modal Framework. IN IEEE Transactions on Intelligent Transportation Systems, 18.2397-2412.

[Bibtex]`@ARTICLE{Dotoli20172397, author={Dotoli, M. and Zgaya, H. and Russo, C. and Hammadi, S.}, title={A Multi-Agent Advanced Traveler Information System for Optimal Trip Planning in a Co-Modal Framework}, journal={IEEE Transactions on Intelligent Transportation Systems}, year={2017}, volume={18}, number={9}, pages={2397-2412}, doi={10.1109/TITS.2016.2645278}, art_number={7829278}, note={cited By 17}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85010672602&doi=10.1109%2fTITS.2016.2645278&partnerID=40&md5=b70a67bc023c289ae5cecf8b537ae41f}, abstract={We present an advanced traveler information system (ATIS) for public and private transportation, including vehicle sharing and pooling services. The ATIS uses an agent-based architecture and multi-objective optimization to answer trip planning requests from multiple users in a co-modal setting, considering vehicle preferences and conflicting criteria. At each set of users' requests, the transportation network is represented by a co-modal graph that allows decomposing the trip planning problem into smaller tasks: the shortest routes between the network nodes are determined and then combined to obtain possible itineraries. Using multi-objective optimization, the set of user-vehicle-route combinations according to the users' preferences is determined, ranking all possible route agents' coalitions. The ATIS is tested for the real case study of the Lille metropolitan area (Nord Pas de Calais, France). © 2000-2011 IEEE.}, author_keywords={Advanced traveler information system; co-modal transport; directed graphs; multi-agent systems; optimization; private transport; public transport; trip planning}, keywords={Advanced public transportation systems; Information systems; Multi agent systems; Multiobjective optimization; Transportation routes; Vehicles, Agent based architectures; Metropolitan area; Multiple user; Network node; Private transportation; Shortest route; Transportation network; Trip planning, Advanced traveler information systems}, references={Abdel-Aty, M.A., Abdalla, M.F., Examination of multiple mode/route-choice paradigms under ATIS (2006) IEEE Trans. Intell. Transp. Syst., 7 (3), pp. 332-348. , Sep; Adler, J.L., Satapathy, G., Manikonda, V., Bowles, B., Blue, V.J., A multi-agent approach to cooperative traffic management and route guidance (2005) Transp. Res. B, Methodol., 39 (4), pp. 297-318; Arentze, T.A., Adaptive personalized travel information systems: A Bayesian method to learn users' personal preferences in multimodal transport networks (2013) IEEE Trans. Intell. Transp. Syst., 14 (4), pp. 1957-1966. , Dec; Ayed, H., Galvez-Fernandez, C., Habbas, Z., Khadraoui, D., Solving time-dependent multimodal transport problems using a transfer graph model (2011) Comput. Ind. Eng., 61 (2), pp. 391-401; Bellifemine, F., Caire, G., Trucco, T., Rimassa, G., (2015) JADE Programmer's Guide, , http://jade.tilab.com/doc/programmersguide.pdf, accessed on Oct. 14; Borole, N., Rout, D., Goel, N., Vedagiri, P., Mathew, T.V., Multimodal public transit trip planner with real-time transit data (2013) Procedias-Soc. Behavioral Sci., 104, pp. 775-784. , Dec; Chen, B., Cheng, H.H., A review of the applications of agent technology in traffic and transportation systems (2010) IEEE Trans. Intell. Transp. Syst., 11 (2), pp. 485-497. , Jun; Chen, C., Zhang, D., Guo, B., Ma, X., Pan, G., Wu, Z., TripPlanner: Personalized trip planning leveraging heterogeneous crowdsourced digital footprints (2015) IEEE Trans. Intell. Transp. Syst., 16 (3), pp. 1259-1273. , Jun; Chen, G., Wu, S., Zhou, J., Tung, A.K.H., Automatic itinerary planning for traveling services (2014) IEEE Trans. Knowl. Data Eng., 26 (3), pp. 514-527. , Mar; Chiu, D.K.W., Lee, O.K.F., Leung, H.-F., Au, E.W.K., Wong, M.C.W., A multi-modal agent based mobile route advisory system for public transport network (2005) Proc. 38th Annu. Hawaii Int. Conf. Syst. Sci, p. 92b. , Jan; Dotoli, M., Epicoco, N., Falagario, M., Cavone, G., A timed Petri nets model for performance evaluation of intermodal freight transport terminals (2016) IEEE Trans. Autom. Sci. Eng., 13 (2), pp. 842-857. , Apr; Dotoli, M., Hammadi, S., Jeribi, K., Russo, C., Zgaya, H., A multi-agent decision support system for optimization of co-modal transportation route planning services (2013) Proc. IEEE CDC, pp. 911-916. , Florence, Italy, Dec; Foo, H.M., Leong, H.W., Lao, Y., Lau, H.C., A multi-criteria, multi-modal passenger route advisory system (1999) Proc. IES-CTR, pp. 1-16; Genin, T., Aknine, S., Coalition formation strategies for selfinterested agents in task oriented domains (2010) Proc. IEEE/WIC/ACM Int. Conf. Web Intell. Intell. Agent Technol, pp. 205-212. , Aug; (2015), https://developers.google.com/maps/, Google Developers. Google Maps, accessed on Oct. 14; Götzenbrucker, G., Köhl, M., Advanced traveller information systems for intelligent future mobility: The case of 'Anachb' in Vienna (2012) IET Intell. Transp. Syst., 6 (4), pp. 494-501. , Dec; Houda, M., Khemaja, M., Oliveira, K., Abed, M., A public transportation ontology to support user travel planning (2010) Proc. IEEE 4th Int. Conf. Res. Challenges Inf. Sci, pp. 127-136. , Nice, France, May; Hyytiã, E., Penttinen, A., Sulonen, R., Non-myopic vehicle and route selection in dynamic DARP with travel time and workload objectives (2012) Comput. Oper. Res., 39 (12), pp. 3021-3030; Jariyasunant, J., Work, D.B., Kerkez, B., Sengupta, R., Glaser, S., Bayen, A., Mobile transit trip planning with real time data (2010) Transp. Res. Board Annu. Meeting, pp. 1-17. , Washington, DC, USA; Jeribi, K., Mejiri, H., Zgaya, H., Hammadi, S., Vehicle sharing services optimization based on multi-agent approach (2011) Proc. 18th IFAC World Congr, pp. 13040-13045. , Jan; Jeribi, K., Mejri, H., Zgaya, H., Hammadi, S., Distributed graphs for solving co-modal transport problems (2011) Proc. IEEE 14th Int. Conf. Intell. Transp. Syst, pp. 1150-1155. , Oct; Jeribi, K., Zgaya, H., Zoghlami, N., Hammadi, S., Distributed architecture for a co-modal transport system (2011) Proc. IEEE Int. Conf. Sys., Man, Cybern, pp. 2797-2802. , Anchorage, AK, USA, Oct; Kumar, P., Singh, V., Reddy, D., Advanced traveler information system for Hyderabad city (2005) IEEE Trans. Intell. Transp. Syst., 6 (1), pp. 26-37. , Mar; Li, Q., Kurt, C.E., GIS-based itinerary planning system for multimodal and fixed-route transit network (2000) Proc. MID-Continent Transp. Symp., pp. 47-50; Li, L., Zhang, H., Wang, X., Lu, W., Mu, Z., Urban transit coordination using an artificial transportation system (2011) IEEE Trans. Intell. Transp. Syst., 12 (2), pp. 374-383. , Jun; (2015), http://www.ligne-bcd.com/, Ligne BCD, accessed on Oct. 14; (2015), http://www.lilasautopartage.com/, Lilas, accessed on Oct. 14; Michalewicz, Z., Fogel, D.B., (2004) How to Solve It: Modern Heuristics, , Berlin, Germany: Springer-Verlag; Nuzzolo, A., Crisalli, U., Comi, A., Rosati, L., Individual behavioural models for personal transit pre-trip planners (2015) Transp. Res. Procedia, 5, pp. 30-43. , Dec; Park, K., Bell, M.G.H., Kaparias, L., Bogenberger, K., Adaptive route choice model for intelligent route guidance using a rulebased approach (2007) Transp. Res. Rec., J. Transp. Res. Board, 2000, pp. 88-97. , Nov; (2015), http://www.ratp.fr/, accessed on Oct. 14; Rehrl, K., Bruntsch, S., Mentz, H.J., Assisting multimodal travelers: Design and prototypical implementation of a personal travel companion (2007) IEEE Trans. Intell. Transp. Syst., 8 (1), pp. 31-42. , Mar; Rothkrantz, L., Datcu, D., Beelen, M., Personal intelligent travel assistant a distributed approach (2005) Proc. Int. Conf. Artif. Intell., pp. 1-7; Russell, S., Norvig, P., (2009) Artificial Intelligence: A Modern Approach, , Upper Saddle River, NJ, USA: Pearson Education; Satunin, S., Babkin, E., A multi-agent approach to intelligent transportation systems modeling with combinatorial auctions (2014) Expert Syst. Appl., 41 (15), pp. 6622-6633; (2015), http://www.sncf.com/en/passengers, accessed on Oct. 14; Su, J.-M., Chang, C.-H., Ho, W.-C., Development of trip planning systems on public transit in Taiwan (2008) Proc. IEEE Int. Conf. Netw., Sens. Control, pp. 791-795. , Hainan, China, Apr; (2015), http://www.transpole.fr/, accessed on Oct. 14; Tumas, G., Ricci, F., Personalized mobile city transport advisory system (2009) Information and Communication Technologies in Tourism, pp. 173-184. , Vienna, Austria: Springer; (2015), http://www.vlille.fr/, V'Lille, accessed on Oct. 14; Yang, J., Luo, Z., Coalition formation mechanism in multi-agent systems based on genetic algorithms (2007) J. Appl. Soft Comput., 7 (2), pp. 561-568; Yin, M., Griss, M., SCATEAgent: Context-aware software agents for multi-modal travel (2005) Proc. Appl. Agent Technol. Traffic Transp., pp. 69-84; Wang, J., Kaempke, T., Shortest route computation in distributed systems (2004) Comput. Oper. Res., 31 (10), pp. 1621-1633; Wang, F.Y., Mirchandani, P.B., Tang, S., Guest editorial advanced traveler information systems and vision-based techniques for ITS (2005) IEEE Trans. Intell. Transp. Syst., 6 (1), pp. 1-4. , Mar; Zhang, J.W., Liao, F.X., Arentze, T., Timmermans, H., A multimodal transport network model for advanced traveler information system (2011) Proc.-Social Behavioral Sci., 20, pp. 313-322; Zografos, K.G., Androutsopoulos, K.N., Algorithms for itinerary planning in multimodal transportation networks (2008) IEEE Trans. Intell. Transp. Syst., 9 (1), pp. 175-184. , Mar; Zolfpour-Arokhlo, M., Selemat, A., Hashim, S.Z.M., Route planning model of multi-agent system for a supply chain management (2013) Expert Syst. Appl., 40 (5), pp. 1505-1518}, document_type={Article}, source={Scopus}, }`

- Carli, R. & Dotoli, M. (2017) Bi-level programming for the energy retrofit planning of street lighting systems IN Proceedings of the 2017 IEEE 14th International Conference on Networking, Sensing and Control, ICNSC 2017., 543-548.

[Bibtex]`@CONFERENCE{Carli2017543, author={Carli, R. and Dotoli, M.}, title={Bi-level programming for the energy retrofit planning of street lighting systems}, journal={Proceedings of the 2017 IEEE 14th International Conference on Networking, Sensing and Control, ICNSC 2017}, year={2017}, pages={543-548}, doi={10.1109/ICNSC.2017.8000150}, art_number={8000150}, note={cited By 1}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85028509849&doi=10.1109%2fICNSC.2017.8000150&partnerID=40&md5=7aa2cad3d9d4ab8f45ecac5cd40a808f}, abstract={This paper addresses strategic decision making issues for the energy management of urban street lighting. We propose a hierarchical decision procedure that supports the energy manager in determining the optimal energy retrofit plan of an existing public street lighting system throughout a wide urban area. A bi-level programming model integrates several decision making units, each focusing on the energy optimization of a specific limited zone lighting system, and a central decision unit, aiming at a fair distribution of actions among these various systems, while ensuring an efficient use of public funds. We apply the technique to the case study of the city of Bari (Italy), to demonstrate the applicability and efficiency of the proposed optimization model. © 2017 IEEE.}, author_keywords={Bilevel programming; Decision support system; Energy efficiency; Energy management; Hierarchical optimization; Street lighting}, keywords={Artificial intelligence; Decision making; Decision support systems; Energy efficiency; Energy management; Lighting; Lighting fixtures; Optimization; Retrofitting; Street lighting; Urban planning, Bi-level programming; Bilevel programming models; Decision making unit; Hierarchical decisions; Hierarchical optimization; Optimization modeling; Strategic decision making; Street lighting system, Energy management systems}, references={Adamo, F., Attivissimo, F., Cavone, F., Di Nisio, A., Spadavecchia, M., Channel characterization of an open source energy meter (2014) IEEE Trans. Instrum. Meas., 63 (5), pp. 1106-1115. , May; Carli, R., Dotoli, M., Pellegrino, R., A hierarchical decision making strategy for the energy management of smart cities (2016) IEEE Trans. Autom. Sci. Eng; Jollands, N., Waide, P., Ellis, M., The 25 IEA energy efficiency policy recommendations to the G8 gleneagles plan of action (2010) Energy Policy, 38, pp. 6409-6418; De Paz, J.F., Bajo, J., Rodríguez, S., Villarrubia, G., Corchado, J.M., Intelligent system for lighting control in smart cities (2016) Information Sciences, 372, pp. 241-255; Hyari, K.H., Khelifi, A., Katkhuda, H., Multiobjective optimization of roadway lighting projects (2016) Journal of Transportation Engineering, p. 04016024; Rabaza, O., Gómez-Lorente, D., Pérez-Ocón, F., Peña-García, A., A simple and accurate model for the design of public lighting with energy efficiency functions based on regression analysis (2016) Energy, 107, pp. 831-842; Sdziwy, A., A new approach to street lighting design (2016) Leukos, 12 (3), pp. 151-162; Sdziwy, A., Kotulski, L., Towards highly energy-efficient roadway lighting (2016) Energies, 9 (4), p. 263; Liu, G., Sustainable feasibility of solar photovoltaic powered street lighting systems (2014) INT J ELEC POWER, 56, pp. 168-174; Lagorse, J., Paire, D., Miraoui, A., Sizing optimization of a stand-alone street lighting system powered by a hybrid system using fuel cell, PV and battery (2009) Renewable Energy, 34 (3), pp. 683-691; Shahzad, G., Yang, H., Ahmad, A.W., Lee, C., Energy-efficient intelligent street lighting system using traffic-adaptive control (2016) IEEE Sensors Journal, 16 (13), pp. 5397-5405; Beccali, M., Improvement of energy efficiency and quality of street lighting in South Italy as an action of Sustainable Energy Action Plans. the case study of Comiso (RG) (2015) Energy, 92, pp. 394-408; Gutierrez-Escolar, A., Castillo-Martinez, A., Gomez-Pulido, J.M., Gutierrez-Martinez, J.M., González-Seco, E.P.D., Stapic, Z., A review of energy efficiency label of street lighting systems (2016) Energy Efficiency, pp. 1-18; Carli, R., Dotoli, M., Pellegrino, R., Ranieri, L., A decision making technique to optimize a building stock energy efficiency (2016) IEEE Trans. Syst., Man, Cybern., Syst; Carli, R., Dotoli, M., Andria, G., Lanzolla, A.M.L., Bi-level programming for the strategic energy management of a smart city (2016) Proc. IEEE EESMS 2016, pp. 1-6. , Bari; Carli, R., Dotoli, M., Pellegrino, R., ICT and optimization for the energy management of smart cities: The street lighting decision panel (2015) Proc. IEEE ETFA2015, , Luxembourg, September 8-11; Dempster, M.A.H., Fisher, M.L., Jansen, L., Lageweg, B.J., Lenstra, J.K., Kan, R., Rinnooy Kan, A.H.G., Analytical evaluation of hierarchical planning systems (1981) Operations Research, 29 (4), pp. 707-716; Kalashnikov, V.V., Dempe, S., Pérez-Valdés, G.A., Kalashnykova, N.I., Camacho-Vallejo, J.F., Bilevel programming and applications (2015) Mathematical Problems in Engineering; Sherali, H.D., Alameddine, A., A new reformulationlinearization technique for bilinear programming problems (1992) Journal of Global Optimization, 2 (4), pp. 379-410; Gümü, Z.H., Floudas, C.A., Global optimization of mixed-integer bilevel programming problems (2005) Computational Management Science, 2 (3), pp. 181-212}, document_type={Conference Paper}, source={Scopus}, }`

- Carli, R. & Dotoli, M. (2017) A decentralized control strategy for energy retrofit planning of large-scale street lighting systems using dynamic programming IN IEEE International Conference on Automation Science and Engineering., 1196-1200.

[Bibtex]`@CONFERENCE{Carli20171196, author={Carli, R. and Dotoli, M.}, title={A decentralized control strategy for energy retrofit planning of large-scale street lighting systems using dynamic programming}, journal={IEEE International Conference on Automation Science and Engineering}, year={2017}, volume={2017-August}, pages={1196-1200}, doi={10.1109/COASE.2017.8256266}, note={cited By 2}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85044970276&doi=10.1109%2fCOASE.2017.8256266&partnerID=40&md5=7f8ba32dffa1946d990b4fa9bdfa7a59}, abstract={This paper addresses the energy management of large-scale urban street lighting systems. We propose a multi-stage decision-making procedure that supports the energy manager in determining the optimal energy retrofit plan of an existing public street lighting system. The problem statement is based on a quadratic integer programming formulation and aims at simultaneously reducing the energy consumption, ensuring an optimal allocation of the retrofit actions, and efficiently using the available budget. The proposed solution relies on a decentralized optimization algorithm that is based on discrete dynamic programming. The methodology is applied to a real street lighting system in the city of Bari, Italy. © 2017 IEEE.}, keywords={Budget control; Decision making; Energy management; Energy management systems; Energy utilization; Integer programming; Lighting fixtures; Retrofitting; Street lighting, Decentralized optimization; Decision making procedure; Energy managers; Optimal allocation; Problem statement; Quadratic integer programming; Retrofit actions; Street lighting system, Dynamic programming}, references={Radulovic, D., Skok, S., Kirincic, V., Energy efficiency public lighting management in the cities (2011) Energy, 36 (4), pp. 1908-1915; Pizzuti, S., Annunziato, M., Moretti, F., Smart street lighting management (2013) Energy Efficiency, 6 (3), pp. 607-616; Carli, R., Dotoli, M., Pellegrino, R., A hierarchical decision making strategy for the energy management of smart cities (2016) IEEE Trans. Aut. Sci. Eng; Gomez-Lorente, D., Rabaza, O., Espin, A., Pena-Garcia, A., Optimization of efficiency and energy saving in public lighting with multi-objective evolutionary algorithms (2013) Proc. ICREPQ, , March; Rabaza, O., Pena-Garcia, A., Perez-Ocon, F., Gomez-Lorente, D., A simple method for designing efficient public lighting, based on new parameter relationships (2013) ExpertSystAppl., 40 (18), pp. 7305-7315; Huang, S.C., Lee, L.L., Jeng, M.S., Hsieh, Y.C., Assessment of energy-efficient LED street lighting through large-scale demonstration (2012) Proc. Int. Conf. Renewable Energy Research and Applications, pp. 1-5; Burgos-Payan, M., Correa-Moreno, F., Riquelme-Santos, J., Improving the energy efficiency of street lighting. A case in the South of Spain (2012) Proc. Int. Conf. European Energy Market, pp. 1-8. , 10-12 May; Beccali, M., Improvement of energy efficiency and quality of street lighting in south Italy as an action of sustainable energy action plans. The case study of Comiso (RG) (2015) Energy, 92, pp. 394-408; Carli, R., Dotoli, M., Pellegrino, R., ICT and optimization for the energy management of smart cities: The street lighting decision panel (2015) Proc. IEEEETFA, , Luxembourg, September; Carli, R., Dotoli, M., Cianci, E., An optimization tool for energy efficiency of street lighting systems in smart cities (2017) IFAC World Congress, , July 9-14, 2017, Toulouse, France; Pisinger, D., The quadratic knapsack problem-a survey (2007) Discrete Applied Mathematics, 155 (5), pp. 623-648; Sniedovich, M., (2010) Dynamic Programming: Foundations and Principles, , CRC press; Boyd, S., Vandenberghe, L., (2004) Convex Optimization, , Cambridge University Press, UK; Bertsekas, D.P., (1995) Dynamic Programming and Optimal Control, 1 (2). , Belmont, MA: Athena Scientific; Achterberg, T., SCIP: Solving constraint integer programs (2009) Math. Progr. Comp., 1, pp. 1-41}, document_type={Conference Paper}, source={Scopus}, }`

- Carli, R. & Dotoli, M. (2017) Using the distributed proximal alternating direction method of multipliers for smart grid monitoring IN IEEE International Conference on Automation Science and Engineering., 418-423.

[Bibtex]`@CONFERENCE{Carli2017418, author={Carli, R. and Dotoli, M.}, title={Using the distributed proximal alternating direction method of multipliers for smart grid monitoring}, journal={IEEE International Conference on Automation Science and Engineering}, year={2017}, volume={2017-August}, pages={418-423}, doi={10.1109/COASE.2017.8256140}, note={cited By 2}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85044966539&doi=10.1109%2fCOASE.2017.8256140&partnerID=40&md5=dbecf71fd06d5b7c380c0ef1f2547de4}, abstract={Efficient and effective monitoring represents the starting point for a reliable and secure smart grid. Given the increasing size and complexity of power networks and the pressing concerns on privacy and robustness, the development of intelligent and flexible distributed monitoring systems represents a crucial issue in both structuring and operating future grids. In this context, this paper presents a distributed optimization framework for use in smart grid monitoring. We propose a distributed algorithm based on ADMM (Alternating Direction Method of Multipliers) for use in large scale optimization problems in smart grid monitoring. The proposed solution is based upon a local-based optimization process, where a limited amount of information is exchanged only between neighboring nodes in a locally broadcast fashion. Applying the approach to two illustrating examples demonstrates it allows exploiting the scalability and efficiency of distributed ADMM for distributed smart grid monitoring. © 2017 IEEE.}, author_keywords={ADMM; distributed optimization; monitoring; sensors network; smart grid}, keywords={Electric power transmission networks; Monitoring, ADMM; Alternating direction method of multipliers; Amount of information; Distributed monitoring systems; Distributed optimization; Large-scale optimization; Sensors network; Smart grid, Smart power grids}, references={Zima, M., Larsson, M., Korba, P., Rehtanz, C., Andersson, G., Design aspects for wide-area monitoring and control systems (2005) Proceedings Ofthe IEEE, 93 (5), pp. 980-996; Adamo, F., Cavone, G., Di Nisio, A., Lanzolla, A., Spadavecchia, M., A proposal for an open source energy meter (2013) Proc. IEEE i2MTC, pp. 488-492. , May 6-9. Minneapolis, MN, USA; Carli, R., Dotoli, M., A distributed control algorithm for waterfilling of networked control systems via consensus IEEE Control Systems Letters, PP (99), pp. I-1; Chakrabarti, S., Kyriakides, E., Bi, T., Cai, D., Terzija, V., Measurements get together (2009) IEEE Power Energy Mag., 7 (1), pp. 41-49; Carli, R., Dotoli, M., Pellegrino, R., A hierarchical decision making strategy for the energy governance of smart cities (2016) IEEE Trans. Autom. Sci. Eng.; Kayastha, N., Niyato, D., Hossain, E., Han, Z., Smart grid sensor data collection, communication, and networking: A tutorial (2014) Wireless Communications and Mobile Computing, 14 (11), pp. 1055-1087; Santacana, E., Rackliffe, G., Tang, L., Feng, X., Getting smart (2010) IEEE Power Energy Mag., 8 (2), pp. 41-48; Gharavi, H., Ghafurian, R., (2011) Smart Grid: The Electric Energy System Ofthe Future, 99. , IEEE; Carli, R., Dotoli, M., Cooperative distributed control for the energy scheduling of smart homes with shared energy storage and renewable energy source (2017) IFAC 2017 WC, , Toulouse, France, July 914; Abur, A., Exposito, A.G., (2004) Power System State Estimation, Theory and Implementation, , Marcel Dekker; Liu, L., Han, Z., Multi-block ADMM for big data optimization in smart grid (2015) Proc. IEEE ICNC, p. 556561. , Garden Grove, CA; Di Bisceglie, M., Galdi, C., Vaccaro, A., Villacci, D., Cooperative sensor networks for voltage quality monitoring in smart grids (2009) PowerTech, 2009 IEEE Bucharest, 423, pp. 1-6. , June. IEEE; Wei, E., Ozdaglar, A., Distributed alternating direction method of multipliers (2012) Proc. IEEE CDC, pp. 5445-5450. , December; Formato, G., Loia, V., Paciello, V., Vaccaro, A., A decentralized and self organizing architecture for wide area synchronized monitoring of smart grids (2013) J HiGH SPEED NETW, 19 (3), pp. 165-179; Capriglione, D., Ferrigno, L., Paciello, V., Pietrosanto, A., Vaccaro, A., Experimental characterization of consensus protocol for decentralized smart grid metering (2016) Measurement, 77, pp. 292-306; Valverde, G., Terzja, V., PMU-based multi-area state estimation with low data exchange (2010) Proc. IEEE PES ISGT Europe, pp. 1-7. , October; Zhao, L., Abur, A., Multiarea state estimation using synchronized phasor measurements (2005) IEEE Trans. Power Syst., 20 (2), pp. 611-617. , May; Jiang, W., Vittal, V., Heydt, G.T., A distributed state estimator utilizing synchronized phasor measurements (2007) IEEE Trans. Power Syst., 22 (2), pp. 563-571. , May; Kekatos, V., Giannakis, G.B., Distributed robust power system state estimation (2013) IEEE Trans. Power Syst., 28 (2), pp. 1617-1626; Kar, S., Hug, G., Mohammadi, J., Moura, J.M.F., Distributed state estimation and energy management in smart grids: A consensus {+} innovations approach (2014) IEEE 1. Sel. Topics Signal Process., 8 (6), pp. 1022-1038. , Dec; Gomez-Exposito, A., De La Villa Jaen, A., Gomez-Quiles, C., Rousseaux, P., Van Cutsem, T., A taxonomy of multi-area state estimation methods (2011) ELECTR POW SYST RES, 81 (4), p. 10601069; Boyd, S., Parikh, N., Chu, E., Peleato, B., Eckstein, J., Distributed optimization and statistical learning via the alternating direction method of multipliers (2010) Foundation and Trends in Machine Learning, 3 (1), pp. 1-122. , Nov; Glowinski, R., Marocco, A., Sur I'approximation par elements finis d'ordre un, et la resolution part penalisation-dualite, d'une class de problems de Dirichlet non lineaires (1975) Rev. Francaise Automat, Informat. Recherche Ooperationalle Ser. Rouge, R2, pp. 41-76; Meng, D., Fazel, M., Mesbahi, M., Proximal alternating direction method of multipliers for distributed optimization on weighted graphs (2015) Proc. IEEE CDC, pp. 1396-1401. , December; Deng, W., Lai, M.J., Peng, Z., Yin, W., (2013) Parallel Multiblock ADMM with 0 (Ilk) Convergence, , arXiv preprint arXiv: 1312. 3040; Mateos, G., Schizas, I.D., Giannakis, G.B., Performance analysis of the consensus-based distributed LMS algorithm (2009) EURASIP J Adv Signal Process, 2009 (1), pp. 1-19; Bertsekas, D.P., Tseng, P., Partial proximal minimization algorithms for convex pprogramming (1994) SIAM J Optim, 4 (3), pp. 551-572}, document_type={Conference Paper}, source={Scopus}, }`

- Carli, R., Dotoli, M. & Cianci, E. (2017) An optimization tool for energy efficiency of street lighting systems in smart cities IN IFAC-PapersOnLine., 14460-14464.

[Bibtex]`@CONFERENCE{Carli201714460, author={Carli, R. and Dotoli, M. and Cianci, E.}, title={An optimization tool for energy efficiency of street lighting systems in smart cities}, journal={IFAC-PapersOnLine}, year={2017}, volume={50}, number={1}, pages={14460-14464}, doi={10.1016/j.ifacol.2017.08.2292}, note={cited By 26}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85042493832&doi=10.1016%2fj.ifacol.2017.08.2292&partnerID=40&md5=b1ee03a53ee4a57450cc0e590002a9e6}, abstract={This paper develops a decision making tool to support the public decision maker in selecting the optimal energy retrofit interventions on an existing street lighting system. The problem statement is based on a quadratic integer programming formulation and deals with simultaneously reducing the energy consumption and ensuring an optimal allocation of the retrofit actions among the street lighting subsystems. The methodology is applied to a real street lighting system in Bari, Italy. The obtained results demonstrate that the approach effectively supports the city governance in making decisions for the optimal energy management of the street lighting. © 2017}, author_keywords={Decision Making; Distribution Management Systems; Energy; Optimization; Smart Cities; Street Lighting}, keywords={Energy efficiency; Energy utilization; Integer programming; Lighting fixtures; Optimization; Retrofitting; Smart city; Street lighting, Decision making tool; Distribution management systems; Energy; Optimal allocation; Optimization tools; Problem statement; Quadratic integer programming; Street lighting system, Decision making}, references={Achterberg, T., SCIP: solving constraint integer programs (2009) Math. Progr. Comp., 1, pp. 1-41; Adamo, F., Cavone, G., Di, A., (2013), pp. 488-492. , Nisio, A. Lanzolla and M. Spadavecchia “A proposal for an open source energy meter,” Proc. IEEE I2MTC, Minneapolis, MN, USA, May 6-9; Beccali, M., Improvement of energy efficiency and quality of street lighting in South Italy as an action of Sustainable Energy Action Plans. The case study of Comiso (RG) (2015) Energy, 92, pp. 394-408; Burgos-Payan, M., Correa-Moreno, F., Riquelme-Santos, J., (2012), “Improving the energy efficiency of street lighting. A case in the South of Spain” Proc. IEEE EEM, pp.1,8, May; Carli, R., Dotoli, M., Pellegrino, R., Ranieri, L., (2013), pp. 1288-1293. , “Measuring and Managing the Smartness of Cities: a Framework for Classifying Performance Indicators”, Proc. IEEE Conf. Sys. Man Cyber; Carli, R., Dotoli, M., Pellegrino, R., (2015), “ICT and optimization for the energy management of smart cities: The street lighting decision panel”, Proc. IEEE ETFA, Luxembourg, September 8-11; Carli, R., Dotoli, M., Pellegrino, R., (2016), ; Ranieri, L., “A decision making technique to optimize a building stock energy efficiency”, IEEE Trans. Sys. Man Cyb.: Sys. doi: 10.1109/TSMC.2016.2521836; Carli, R., Dotoli, M., Pellegrino, R., (2016), “A Hierarchical Decision Making Strategy for the Energy Management of Smart Cities”, IEEE Trans. Autom. Sci. Eng. doi: 10.1109/TASE.2016.2593101; Carli, R., Dotoli, M., Andria, G., Lanzolla, A.M.L., Bi-level programming for the strategic energy management of a smart city (2016) Proc. IEEE EESMS, Bari, pp. 1-6; Covitti, A., Delvecchio, G., Neri, F., Ripoli, A., Sylos Labini, M., Road Lighting Installation Design to Optimize Energy Use by Genetic Algorithms (2005) Proc. EUROCON, 2, pp. 1541-1544; Gallo, G., Hammer, P.L., Simeone, B., Quadratic knapsack problems (1980) Combinatorial Optimization, pp. 132-149. , Springer Berlin Heidelberg; Gargiulo, C., Natale, A., Russo, L., (2015), pp. 1-6. , “Smart community for the smart governance of the urban environment” Proc. IEEE ISC2, Guadalajara; Gómez-Lorente, D., Rabaza, O., Espín, A., Peña-García, A., (2013), “Optimization of efficiency and energy saving in public lighting with multi-objective evolutionary algorithms”, Proc. ICREPQ, 20-22 March; Huang, S.C., Lee, L.L., Jeng, M.S., Hsieh, Y.C., (2012), pp. 1-5. , “Assessment of energy-efficient LED street lighting through large-scale demonstration,” Proc. IEEE ICRERA; Kotulski, L., Towards Highly Energy-Efficient Roadway Lighting (2016) Energies, 9 (4); Novak, T., Pollhammer, K., Zeilinger, H., Schaat, S., Intelligent streetlight management in a smart city (2014) IEEE ETFA, 2014, pp. 1-8. , (September); Pizzuti, S., Annunziato, M., Moretti, F., Smart street lighting management (2013) Energy Efficiency, 6 (3), pp. 607-616; Rabaza, O., Palomares-Muñoz, Z.E., Peña-García, A., Gómez-Lorente, D., Arán-Carrión, J., Aznar-Dols, F., Espín-Estrella, A., (2014), “Multi-objective Optimization applied to Photovoltaic Street Lighting Systems”, Proc. IEEE ICREPQ, X, No.12, April; Radulovic, D., Skok, S., Kirincic, V., “Energy efficiency public lighting management in the cities” (2011) Energy, 36 (4), pp. 1908-1915; Ramadhani, F., Bakar, K.A., Shafer, M.G., (2013) Optimization of standalone street light system with consideration of lighting control, 588, pp. 9-11. , Proc. IEEE TAEECE, pp.583, May; Sittoni, A., Brunelli, D., Macii, D., Tosato, P., Petri, D., (2015), pp. 1-6. , “Street lighting in smart cities: A simulation tool for the design of systems based on narrowband PLC,” Proc. IEEE ISC2, Guadalajara; Wu, Y., Shi, C., Zhang, X., Yang, W., (2010) Design of new intelligent street light control system, 1427, pp. 9-11. , Proc. IEEE ICCA, pp.1423, June}, document_type={Conference Paper}, source={Scopus}, }`

- Dotoli, M. & Epicoco, N. (2017) A Vehicle Routing Technique for Hazardous Waste Collection IN IFAC-PapersOnLine., 9694-9699.

[Bibtex]`@CONFERENCE{Dotoli20179694, author={Dotoli, M. and Epicoco, N.}, title={A Vehicle Routing Technique for Hazardous Waste Collection}, journal={IFAC-PapersOnLine}, year={2017}, volume={50}, number={1}, pages={9694-9699}, doi={10.1016/j.ifacol.2017.08.2051}, note={cited By 15}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85031805728&doi=10.1016%2fj.ifacol.2017.08.2051&partnerID=40&md5=da5f048c9dfb57aff3692a450f9360b2}, abstract={Nowadays there is a growing interest in properly managing and collecting waste. Due to major threats on human health and environmental impact, hazardous waste management requires even much more attention. Nonetheless, in the literature there is a lack of techniques specifically devoted to the optimization of such a critical activity, which is characterized by more stringent constraints with respect to the typical municipal solid waste management. To fill this gap, we present a technique to solve the vehicle routing and scheduling problem for hazardous waste collection and disposal. The proposed method allows limiting the distance traveled by road (and therefore operating costs and emissions), enabling to match requests while respecting service time windows and vehicles’ availability. The technique also allows performing what-if analyses to evaluate the benefits arising from future investments in the fleet. The effectiveness of the method is shown by a real case study. © 2017}, author_keywords={Hazardous Waste; Optimization; Scheduling; Vehicle Routing}, keywords={Hazardous materials; Hazards; Health risks; Operating costs; Optimization; Scheduling; Vehicle routing; Vehicles; Waste disposal, Critical activities; Hazardous waste management; Hazardous wastes; Routing techniques; Service time; Stringent constraints; Vehicle routing and scheduling; What-if Analysis, Municipal solid waste}, references={Apaydin, O., Gonullu, M.T., Route time estimation of solid waste collection vehicles based on population density (2011) Glob Nest J, 13 (2), pp. 162-169; Braekers, K., Ramaekers, K., Van Nieuwenhuyse, I., The Vehicle Routing Problem: State of the art classification and review (2016) Comput Ind Eng, 99, pp. 300-313; Buhrkal, K.F., Larsen, A., Ropke, S., The Waste Collection VRP with Time Windows in a city logistic context (2012) Procedia Soc Behav Sci, 39, pp. 241-254; Carli, R., Dotoli, M., Epicoco, N., Angelico, B., Vinciullo, A., Automated evaluation of urban traffic congestion using bus as a probe (2015) Proc 11th IEEE Int Conf Autom Sci Eng, pp. 967-972; Dotoli, M., Epicoco, N., Falagario, M., Angelico, B., Vinciullo, A., A two-step optimization model for the pre- and end-haulage of containers at intermodal freight terminals (2015) Proc 14th Eur Contr Conf, pp. 696-701; Dotoli, M., Epicoco, N., Falagario, M., Sciancalepore, F., A cross-efficiency fuzzy Data Envelopment Analysis technique for performance evaluation of decision making units under uncertainty (2015) Comp. Ind. Eng., 79 (1), pp. 103-114; Dotoli, M., Epicoco, N., (2016) A technique for the optimal management of containers’ drayage at intermodal terminals, , IEEE Int Conf Sys Man Cyber, Budapest (Hungary); Dotoli, M., Epicoco, N., Falagario, M., Seatzu, C., Turchiano, B., A Decision Support System for Optimizing Operations at Intermodal Rail-Road Terminals (2017) IEEE Trans Sys Man Cyber: Sys, 47 (3), pp. 487-501; Galante, G., Aiello, G., Enea, M., Panascia, E., A multi-objective approach to solid waste management (2010) Waste Manage, 30 (8), pp. 1720-1728; Han, H., Ponce-Cueto, E., Waste Collection Vehicle Routing Problem: Literature review (2015) Promet, 27 (4), pp. 345-358; Karadimas, N., Papatzelou, K., Loumos, V.G., Optimal solid waste collection routes identified by the ant colony system algorithm (2007) Waste Manage & Res, 25, pp. 139-147; Kim, B.I., Kim, S., Sahoo, S., Waste Collection Vehicle Routing Problem with Time Windows (2006) Comput Oper Res, 33, pp. 3624-3642; Ma, J., Hipel, K.W., Exploring social dimensions of municipal solid waste management around the globe -A systematic literature review (2016) Waste Manage, 56, pp. 3-12; Markov, I., Varone, S., Bierlaire, M., (2014), Vehicle routing for a complex waste collection problem. 14th Swiss Transp Res Conf; Nuortio, T., Kytojoki, J., Niska, H., Braysy, O., Improved route planning and scheduling of waste collection and transport (2006) Exp Sys Appl, 30 (2), pp. 223-232; Pires, A., Martinho, G., Chang, N., Solid waste management in European countries: A review of systems analysis techniques (2011) J Environ Manage, 92, pp. 1033-1050; Son, L.H., Louati, A., Modeling municipal solid waste collection: A generalized vehicle routing model with multiple transfer stations, gather sites and inhomogeneous vehicles in time windows (2016) Waste Manage, 52, pp. 34-49; Tan, Q., Huang, G.H., Cai, Y.P., Waste management with recourse: An inexact dynamic programming model containing fuzzy boundary intervals in objectives and constraints (2010) J Environ Manage, 91, pp. 1898-1913}, document_type={Conference Paper}, source={Scopus}, }`

- Carli, R. & Dotoli, M. (2017) Cooperative Distributed Control for the Energy Scheduling of Smart Homes with Shared Energy Storage and Renewable Energy Source IN IFAC-PapersOnLine., 8867-8872.

[Bibtex]`@CONFERENCE{Carli20178867, author={Carli, R. and Dotoli, M.}, title={Cooperative Distributed Control for the Energy Scheduling of Smart Homes with Shared Energy Storage and Renewable Energy Source}, journal={IFAC-PapersOnLine}, year={2017}, volume={50}, number={1}, pages={8867-8872}, doi={10.1016/j.ifacol.2017.08.1544}, note={cited By 28}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85031787804&doi=10.1016%2fj.ifacol.2017.08.1544&partnerID=40&md5=9d6a82e7d9f16756a8d263254e537f45}, abstract={This paper presents a distributed control technique for the energy scheduling of a group of interconnected smart city residential users. The proposed model aims at a simultaneous cost-optimal planning of users’ controllable appliances and of the shared storage system charge/discharge and renewable energy source. The distributed control algorithm is based on an iterative procedure combining parametric optimization with the block coordinate descent method. A realistic case study simulated in different scenarios demonstrates that the approach allows fully exploiting the potential of storage systems sharing to reduce individual users’ energy consumption costs and limit the peak average ratio of the energy profiles. © 2017}, author_keywords={Decentralized; Distributed Control; Distribution Management Systems; Energy; Energy Storage Operation; Large scale optimization problems; Planning; Smart Grids}, keywords={Automation; Distributed parameter control systems; Energy storage; Energy utilization; Intelligent buildings; Iterative methods; Natural resources; Planning; Renewable energy resources; Scheduling, Decentralized; Distributed control; Distribution management systems; Energy; Large-scale optimization; Smart grid; Storage operations, Smart power grids}, references={Adika, C.O., Wang, L., Non-Cooperative Decentralized Charging of Homogeneous Households’ Batteries in a Smart Grid (2014) IEEE Trans. Smart Grid, 5 (4), p. 1855. , pp. 1863, July; Arghandeh, R., Woyak, J., Onen, A., Jung, J., Broadwater, R.P., Economic optimal operation of Community Energy Storage systems in competitive energy markets (2014) Applied Energy, 135, pp. 71-80; Attivissimo, F., Di, A., Nisio, A.M., Lanzolla, L., Paul, M., Feasibility of a Photovoltaic–Thermoelectric Generator: Performance Analysis and Simulation Results (2015) IEEE Trans. Instrum. Meas, 64 (5), pp. 1158-1169; Atzeni, I., Ordonez, L.G., Scutari, G., Palomar, D.P., Fonollosa, J.R., Demand-Side Management via Distributed Energy Generation and Storage Optimization (2013) IEEE Trans. Smart Grid, 4 (2), p. 866. , pp. 876; Bertsekas, D.P., (1999) Nonlinear programming, p. 780. , Athena scientific Belmont; Brusco, G., Burgio, A., Menniti, D., Pinnarelli, A., Sorrentino, N., Energy Management System for an Energy District With Demand Response Availability (2014) IEEE Trans. Smart Grid, 5 (5), pp. 2385-2393; Carli, R., Dotoli, M., Pellegrino, R., Ranieri, L., (2013), pp. 1288-1293. , “Measuring and Managing the Smartness of Cities: a Framework for Classifying Performance Indicators” Proc. IEEE Conf. Sys. Man Cyber; Carli, R., Dotoli, M., (2014), pp. 5648-5653. , “Energy Scheduling of a Smart Home under Nonlinear Pricing” Proc. IEEE Int. Conf. Dec. Contr., Dec. 15-17; Carli, R., Dotoli, M., (2015), “A Decentralized Resource Allocation Approach For Sharing Renewable Energy among Interconnected Smart Homes,” IEEE Int. Conf. Dec. Contr, Dec. 15-18; Carli, R., Dotoli, M., Pellegrino, R., (2016), “A Hierarchical Decision Making Strategy for the Energy Management of Smart Cities” IEEE Trans. Aut. Sci. Eng., 19 pp., doi:; Carli, R., Dotoli, M., Pellegrino, R., Ranieri, L., (2016), “A Hierarchical Decision Making Technique to Optimize a Building Stock Energy Efficiency” IEEE Trans. Sys. Man Cyb.: Sys., 14 pp, doi:; Dotoli, M., Epicoco, N., Falagario, M., Sciancalepore, F., Costantino, N., A Nash equilibrium simulation model for the competitiveness evaluation of the auction based day ahead electricity market (2014) Comput. Ind., 65 (4), pp. 774-785; Gkatzikis, L., Iosifidis, G., Koutsopoulos, I., Tassiulas, L., (2014), pp. 103-108. , (November). Collaborative placement and sharing of storage resources in the Smart Grid. IEEE SmartGridComm, Conf. 2014; Graves, F., Jenkins, T., Murphy, D., Opportunities for electricity storage in deregulating markets (1999) The Electricity J., 12 (8), pp. 46-56; Kefayati, M., Baldick, R., (2013), “On optimal operation of storage devices under stochastic market prices, Proc. IEEE Int. Conf. Dec. Contr; Harsha, P., Dahleh, M., (2011), “Optimal sizing of energy storage for efficient integration of renewable energy, Proc. IEEE Int. Conf. Dec. Contr; He, X., Delarue, E., D'haeseleer, W., Glachant, J.M., A novel business model for aggregating the values of electricity storage (2011) Energy Policy, 39 (3), pp. 1575-1585; Jesudasan, R.N., Andrew, L.L., Scheduling long term energy storage (2014) In INFOCOM WKSHPS, 2014, pp. 634-639. , (April); Mégel, O., Mathieu, J.L., Andersson, G., Scheduling distributed energy storage units to provide multiple services under forecast error (2015) INT J ELEC POWER, 72, pp. 48-57; Mohsenian-Rad, A.-H., Wong, V., Jatskevich, J., Schober, R., Leon-Garcia, A., Autonomous demand-side management based on game-theoretic energy consumption scheduling for the future smart grid (2010) IEEE Trans. on Smart Grid, 1 (3), pp. 320-331; Sioshansi, R., Welfare impacts of electricity storage and the implications of ownership structure (2010) Energy J., 31 (2), p. 173; Stallings, W., (2015) Data and Computer Communications, , Pearson Education Limited; Vytelingum, P., Voice, T.D., Ramchurn, S.D., Rogers, A., Jennings, N.R., (2010), 1, pp. 39-46. , (May). Agent-based micro-storage management for the smart grid. Proc. Int. Conf. AAMAS:; Wang, Y., Lin, X., Pedram, M., Adaptive control for energy storage systems in households with photovoltaic modules (2014) IEEE Trans. Smart Grid, 5 (2), pp. 992-1001}, document_type={Conference Paper}, source={Scopus}, }`

- Carli, R., Dotoli, M., Pellegrino, R. & Ranieri, L. (2017) A Decision Making Technique to Optimize a Buildings’ Stock Energy Efficiency. IN IEEE Transactions on Systems, Man, and Cybernetics: Systems, 47.794-807.

[Bibtex]`@ARTICLE{Carli2017794, author={Carli, R. and Dotoli, M. and Pellegrino, R. and Ranieri, L.}, title={A Decision Making Technique to Optimize a Buildings' Stock Energy Efficiency}, journal={IEEE Transactions on Systems, Man, and Cybernetics: Systems}, year={2017}, volume={47}, number={5}, pages={794-807}, doi={10.1109/TSMC.2016.2521836}, art_number={7407645}, note={cited By 42}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85018489196&doi=10.1109%2fTSMC.2016.2521836&partnerID=40&md5=db70236a891233a4576656e616023e08}, abstract={This paper focuses on applying multicriteria decision making tools to determine an optimal energy retrofit plan for a portfolio of buildings. We present a two-step decision making technique employing a multiobjective optimization algorithm followed by a multiattribute ranking procedure. The method aims at deciding, in an integrated way, the optimal energy retrofit plan for a whole stock of buildings, optimizing efficiency, sustainability, and comfort, while effectively allocating the available financial resources to the buildings. The proposed methodology is applied to a real stock of public buildings in Bari, Italy. The obtained results demonstrate that the approach effectively supports the city governance in making decisions for the optimal management of the buildings' energy efficiency. © 2016 IEEE.}, author_keywords={Building management; energy efficiency; multiattribute analysis; multicriteria decision making; multiobjective optimization (MOO); optimization algorithms}, keywords={Buildings; Energy efficiency; Multiobjective optimization; Optimization; Retrofitting, Building management; Financial resources; Multi criteria decision making; Multi-attribute analysis; Multiattribute rankings; Optimal management; Optimization algorithms; Public buildings, Decision making}, references={Alanne, K., Selection of renovation actions using multi-criteria'knapsack' model (2004) Autom. Construct, 13 (3), pp. 377-391; Asadi, E., Da Silva, M.G., Antunes, C.H., Dias, L., Multi-objectiveoptimization for building retrofit strategies: A model and an application (2012) Energy Build, 44, pp. 81-87. , Jan; Asadi, E., Da Silva, M.G., Antunes, C.H., Dias, L., A multi-objectiveoptimization model for building retrofit strategies using TRNSYS simulations, GenOpt and MATLAB (2012) Build. Environ, 56, pp. 370-378. , Oct; (2004) Thermal Environment Conditions for Human Occupancy, pp. 55-2004. , Atlanta:American Society of Heating, Refrigerating., Air-ConditioningEngineers, Inc., ANSI Standard; Barlow, S., Fiala, D., Occupant comfort in UK offices-Howadaptive comfort theories might influence future low energy officerefurbishment strategies (2007) Energy Build, 39 (7), pp. 837-846; Basuroy, S., Chuah, J.W., Jha, N.K., Making buildings energyefficientthrough retrofits: A survey of available technologies (2013) Proc. IEEE Power Energy Soc. Gen. Meeting, pp. 1-5. , Vancouver, BC, Canada, Jul; Bruckner, D., Dillon, T., Hu, S., Palensky, P., Wei, T., Guest editorialspecial section on building automation, smart homes., communities (2014) IEEE Trans. Ind. Informat, 10 (1), pp. 676-679. , Feb; Caccavelli, D., Gugerli, H., TOBUS-A European diagnosis anddecision-making tool for office building upgrading (2002) Energy Build, 34 (2), pp. 113-119; Cao, B., Development of a multivariate regression model for overallsatisfaction in public buildings based on field studies in Beijing andShanghai (2012) Build. Environ, 47, pp. 394-399. , Jan; Carli, R., Deidda, P., Dotoli, M., Pellegrino, R., An urban controlcenter for the energy governance of a smart city (2014) Proc. 19th IEEEInt. Conf. Emerg. Technol. Factory Autom, pp. 1-7. , Barcelona, Spain, Sep; Carli, R., Dotoli, M., Pellegrino, R., Ranieri, L., Measuring and managingthe smartness of cities: A framework for classifying performanceindicators (2013) Proc. IEEE Conf. Syst. Man Cybern, pp. 1288-1293. , Manchester, U. K; Chiang, C.M., Chou, P.C., Lai, C.M., Li, Y.Y., A methodology toassess the indoor environment in care centers for senior citizens (2001) Build. Environ, 36 (4), pp. 561-568; Chiang, C.-M., Lai, C.-M., A study on the comprehensive indicator ofindoor environment assessment for occupant's health in Taiwan (2002) Build. Environ, 37 (4), pp. 387-392; Chuah, J.W., Raghunathan, A., Jha, N.K., ROBESim: A retrofitorientedbuilding energy simulator based on EnergyPlus (2013) Energy Build, 66, pp. 88-103. , Nov; Chwieduk, D., Towards sustainable-energy buildings (2002) Appl. Energy, 76 (1-3), pp. 211-217; Collinge, W., Landis, A.E., Jones, A.K., Schaefer, L.A., Bilec, M.M., Indoor environmental quality in a dynamic life cycle assessment frameworkfor whole buildings: Focus on human health chemical impacts (2013) Build. Environ, 62, pp. 182-190. , Apr; Dallo, G., (2013) Green Energy Audit of Buildings-A Guide for a SustainableEnergy Audit of Buildings, , London, U. K. : Springer-Verlag; Dallo, G., Norese, M.F., Galante, A., Novello, C., A multicriteriamethodology to support public administration decision makingconcerning sustainable energy action plans (2013) Energy, 6 (8), pp. 4308-4330; Deng, S.-M., Burnett, J., A study of energy performance of hotelbuildings in Hong Kong (2000) Energy Build, 31 (1), pp. 7-12; Diakaki, C., Grigoroudis, E., Kolokotsa, D., Towards a multiobjectiveoptimization approach for improving energy efficiency inbuildings (2008) Energy Build, 40 (9), pp. 1747-1754; Diakaki, C., A multi-objective decision model for the improvementof energy efficiency in buildings (2010) Energy, 35 (12), pp. 5483-5496; Djamila, H., Chu, C.-M., Kumaresan, S., Field study of thermalcomfort in residential buildings in the equatorial hot-humid climate ofMalaysia (2013) Build. Environ, 62, pp. 133-142. , Apr; Dotoli, M., Fanti, M.P., Mangini, A.M., Fuzzy multi-objectiveoptimization for network design of integrated e-supply chains (2007) Int. J. Comput. Integr. Manuf., 20 (6), pp. 588-601; Energy performance of buildings-calculation of energy use for spaceheating and cooling, document 13790 (2008) Int. Org. Stand., , Geneva, Switzerland; Ergonomics of the thermal environment-analytical determination andinterpretation of thermal comfort using calculation of the pmv andppd indices and local thermal comfort criteria, document 7730 (2005) Int. Org. Standard, , Geneva, Switzerland; Energy performance of buildings-energy requirements for lighting (2007) European Standard 15193; Heating systems in buildings-method for calculation of system energyrequirements and system efficiencies-Part 3-1: Domestic hot watersystems (2007) Characterisation of Needs, European Standard 15316-3-1; Fanger, P.O., (1970) Thermal Comfort, , Copenhagen Denmark: Danish Tech. Press; Figueira, J., Greco, S., Ehrgott, M., (2005) Multiple Criteria DecisionAnalysis: State of the Art Surveys, , Boston MA USA: Springer; Giffinger, R., Smart cities: Ranking of europeanmedium-sized cities (2007) Centre Regional Sci, , http://www.smartcities.eu/download/smartcitiesfinalreport.pdf, Vienna Univ. Technol., Vienna, Austria, Tech. Rep. [Online]. Available; Hipel, K.W., Jamshidi, M.M., Tien, J.M., White, C.C., III, The future of systems, man., cybernetics: Application domainsand research methods (2007) IEEE Trans. Syst., Man, Cybern. C, Appl. Rev, 37 (5), pp. 726-743. , Sep; Hwang, C.-L., Yoon, K., (1981) Multiple Attribute Decision Making: Methodsand Applications, , New York NY USA: Springer-Verlag; Ishizaka, A., Nemery, P., (2013) Multi-Criteria Decision Analysis: Methodsand Software, , Chichester, U. K. : Wiley; Ergonomics of the thermal environment-analytical determination andinterpretation of thermal comfort using calculation of the PMV andPPD indices and local thermal comfort criteria, document 7730 (2005) Int. Org. Stand., , Geneva, Switzerland; (2011) Standards for SustainableBuilding-School Buildings, , http://www.itaca.org/documenti/news/PROTOCOLLO%20ITACA%202011S250912.pdf, ITACA Protocol [Online]. Available Accessed Jul. 22, 2014; Juan, Y.-K., Gao, P., Wang, J., A hybrid decision support systemfor sustainable office building renovation and energy performanceimprovement (2010) Energy Build, 42 (3), pp. 290-297; Lee, Y.M., Modeling and simulation of building energy performancefor portfolios of public buildings (2011) Proc. Winter Simulat. Conf., pp. 915-927. , Phoenix, AZ, USA, Dec; Lee, W.L., Burnett, J., Customization of GB tool in Hong Kong (2006) Build. Environ, 41 (12), pp. 1831-1846; Li, L., Sun, Z., Dynamic energy control for energy efficiencyimprovement of sustainable manufacturing systems using Markov decisionprocess (2013) IEEE Trans. Syst., Man, Cybern., Syst, 43 (5), pp. 1195-1205. , Sep; List of Prices for Public Works in Apulia Region, , Http://www.regione.puglia.it/www/web/files/operepubbliche/ListinoprezziRegionePugliaEd.2012x.pdf, Oct. 11, 2014 [Online]. Available; Kaklauskas, A., Zavadskas, E.K., Raslanas, S., Multivariant designand multiple criteria analysis of building refurbishments (2005) Energy Build, 37 (4), pp. 361-372; Kolokotsa, D., Diakaki, C., Grigoroudis, E., Stavrakakis, G., Kalaitzakis, K., Decision support methodologies on the energy efficiencyand energy management in buildings (2009) Adv. Build. Energy Res, 3 (1), pp. 121-146; Ma, Z.J., Cooper, P., Daly, D., Ledo, L., Existing building retrofits:Methodology and state-of-the-art (2012) Energy Build, 55, pp. 889-902. , Dec; Malatji, E.M., Zhang, J., Xia, X., A multiple objective optimisationmodel for building energy efficiency investment decision (2013) EnergyBuild, 61, pp. 81-87. , Jun; Malmqvist, T., A Swedish environmental rating tool for buildings (2011) Energy, 36 (4), pp. 1893-1899; Marino, C., Nucara, A., Pietrafesa, M., Proposal of comfort classificationindexes suitable for both single environments and wholebuildings (2012) Build. Environ, 57, pp. 58-67. , Nov; Mui, K.W., Chan, W.T., A new indoor environmental quality equationfor air-conditioned buildings (2005) Archit. Sci. Rev, 48 (1), pp. 41-46; Ncube, M., Riffat, S., Developing an indoor environment quality toolfor assessment of mechanically ventilated office buildings in the UK-A preliminary study (2012) Build. Environ, 53, pp. 26-33. , Jul; Noris, F., Indoor environmental quality benefits of apartmentenergy retrofits (2013) Build. Environ, 68, pp. 170-178. , Oct; Directive2010/31/EU of the European parliament and of the council of19 May 2010 on the energy performance of buildings (recast) (2010) Off. J. Eur. Communities, L153, pp. 13-35. , Energy Performance of Buildings Directive (EPBD) Jun; Pérez-Lombard, L., Ortiz, J., Pout, C., A review on buildings energyconsumption information (2008) Energy Build, 40 (3), pp. 394-398; Perng, Y.-H., Juan, Y.-K., Hsu, H.-S., Genetic algorithm-based decisionsupport for the restoration budget allocation of historical buildings (2007) Build. Environ, 42 (2), pp. 770-778; Roberts, S., Altering existing buildings in the UK (2008) Energy Policy, 36 (12), pp. 4482-4486; Roulet, C.-A., ORME: A multicriteria rating methodology forbuildings (2002) Build. Environ, 37 (6), pp. 579-586; Rysanek, A.M., Choudhary, R., Optimum building energy retrofitsunder technical and economic uncertainty (2013) Energy Build, 57, pp. 324-337. , Feb; Torfi, F., Farahani, R.Z., Rezapour, S., Fuzzy AHP to determinethe relative weights of evaluation criteria and fuzzy TOPSIS to rank thealternatives (2010) Appl. Soft Comput, 10 (2), pp. 520-528; (2008) Evaluation of Energy Need for Space Heating and Cooling, DocumentUNI/TS 11300-1: 2008, CTI-Italian Thermotech, , Committee EnergyEnviron., Milan, Italy; (2008) Evaluation of Primary Energy Need and of System Efficienciesfor Space Heating and Domestic Hot Water Production DocumentUNI/TS 11300-2: 2008, Italian Thermotech, , Committee Energy Environ., Milan, Italy; Wong, L.T., Mui, K.W., An energy performance assessmentfor indoor environmental quality (IEQ) acceptance in air-conditionedoffices (2009) Energy Convers. Manag, 50 (5), pp. 1362-1367; Wulfinghoff, D.R., (1999) Energy Efficiency Manual, , Wheaton MD, USA: Energy Inst. Press; Yang B Li, Y., Yao, R., A method of identifying and weighting indicatorsof energy efficiency assessment in Chinese residential buildings (2010) Energy Policy, 38 (12), pp. 7687-7697; Yang, T., Hung, C.-C., Multiple-attribute decision making methodsfor plant layout design problem (2007) Robot. Comput. Integr. Manuf, 23 (1), pp. 126-137; Zanakis, S.H., Solomon, A., Wishart, N., Dublish, S., Multiattributedecision making: A simulation comparison of select methods (1998) Eur. J. Oper. Res, 107 (3), pp. 507-529; Zhang, W., Reimann, M., A simple augmented-constraintmethod for multi-objective mathematical integer programming problems (2014) Eur. J. Oper. Res, 234 (1), pp. 15-24}, document_type={Article}, source={Scopus}, }`

- Dotoli, M., Epicoco, N. & Falagario, M. (2017) A fuzzy technique for supply chain network design with quantity discounts. IN International Journal of Production Research, 55.1862-1884.

[Bibtex]`@ARTICLE{Dotoli20171862, author={Dotoli, M. and Epicoco, N. and Falagario, M.}, title={A fuzzy technique for supply chain network design with quantity discounts}, journal={International Journal of Production Research}, year={2017}, volume={55}, number={7}, pages={1862-1884}, doi={10.1080/00207543.2016.1178408}, note={cited By 16}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84973872727&doi=10.1080%2f00207543.2016.1178408&partnerID=40&md5=cacc19478807834d6d9ab862bcee9b26}, abstract={This paper proposes a hierarchical technique for Supply Chain Network (SCN) efficiency maximisation under uncertainty composed of three steps. The first step extends a previous fuzzy cross-efficiency Data Envelopment Analysis approach, originally intended for suppliers’ selection, in order to evaluate and rank all the actors in each SCN stage under conflicting nondeterministic criteria. Afterwards, a fuzzy linear integer programming model is stated and solved for each pair of subsequent SCN stages to determine the quantities required from each stakeholder to maximise the overall SCN efficiency while satisfying the estimated demand and respecting the nodes capacity. Finally, a heuristics is applied to limit the exchange of small quantities in the SCN, in which the trade is not economically convenient according to quantity discounts. An illustrative example from the literature shows the technique effectiveness. © 2016 Informa UK Limited, trading as Taylor & Francis Group.}, author_keywords={cross-efficiency; data envelopment analysis; discount policy; fuzzy logic; optimisation; supply chain network design; uncertainty}, keywords={Data envelopment analysis; Efficiency; Integer programming; Supply chains; Uncertainty analysis, Cross efficiency; Discount policy; Optimisations; Supply chain network design; uncertainty, Fuzzy logic}, references={Aissaoui, N., Haouari, E., Hassini, M., Supplier Selection and Order Lot Sizing Modeling: A Review (2007) Computers and Operations Research, 34 (12), pp. 3516-3540; Alborzi, F., Vafaei, H., Gholami, M.H., Esfahani, M.M.S., A Multi-objective Model for Supply Chain Network Design under Stochastic Demand (2011) International Journal of Mechanical, Aerospace, Industrial, Mechatronic and Manufacturing Engineering, 5 (11), pp. 2395-2399; Amin, S.H., Zhang, G., A Three-stage Model for Closed-loop Supply Chain Configuration under Uncertainty (2013) International Journal of Production Research, 51 (5), pp. 1405-1425; Amindoust, A., Ahmed, S., Saghafinia, A., Supplier Selection and Order Allocation Scenarios in Supply Chain: A Review (2013) Engineering Management Review, 2 (3), pp. 75-80; Amirteimoori, A., Kordrostami, S., Production Planning in Data Envelopment Analysis (2012) International Journal of Production Economics, 140, pp. 212-218; Angulo Meza, L., Pereira Estellita Lins, M., Review of Methods for Increasing Discrimination in Data Envelopment Analysis (2002) Annals of Operations Research, 116 (1-4), pp. 225-242; Arshinder, K., Kanda, A., Deshmukh, S.G., A Review on Supply Chain Coordination: Coordination Mechanisms, Managing Uncertainty and Research Directions (2011) Supply Chain Coordination under Uncertainty, pp. 39-82. , Choi T.M., Cheng E.T.C., (eds), Springer Berlin Heidelberg; Azadnia, A.H., Saman, M.Z.M., Wong, K.Y., Sustainable Supplier Selection and Order Lot-sizing: An Integrated Multi-objective Decision-Making Process (2015) International Journal of Production Research, 53 (2), pp. 383-408; Cakravastia, A., Takahashi, K., Integrated Model for Supplier Selection and Negotiation in a Make-to-order Environment (2004) International Journal of Production Research, 42 (21), pp. 4457-4474; Cakravastia, A., Toha, I.S., Nakamura, N., A Two-stage Model for the Design of Supply Chain Networks (2002) International Journal of Production Economics, 80, pp. 231-248; Chan, F.T.S., Chung, S.H., Wadhwa, S., A Heuristic Methodology for Order Distribution in a Demand Driven Collaborative Supply Chain (2004) International Journal of Production Research, 42 (1), pp. 1-19; Charnes, A., Cooper, W.W., Rhodes, E., Measuring the Efficiency of Decision Making Units (1978) European Journal of Operational Research, 2, pp. 429-444; Chen, C.-L., Lee, W.-C., Multi-objective Optimization of Multi-echelon Supply Chain Networks with Uncertain Product Demands and Prices (2004) Computers & Chemical Engineering, 28 (6-7), pp. 1131-1144; Cintron, A., Ravindran, A.R., Ventura, J.A., Multi-criteria Mathematical Model for Designing the Distribution Network of a Consumer Goods Company (2010) Computers & Industrial Engineering, 58 (4), pp. 584-593; Costantino, N., Dotoli, M., Falagario, M., Sciancalepore, F., Fuzzy Network Design of Sustainable Supply Chains (2012) Information Control Problems in Manufacturing, 14 (1), pp. 1284-1289; Costantino, N., Dotoli, M., Falagario, M., Fanti, M.P., Mangini, A.M., Sciancalepore, F., Ukovich, W., A Hierarchical Optimization Technique for the Strategic Design of Distribution Networks (2013) Computers and Industrial Engineering, 66, pp. 849-864; Demirtas, E.A., Üstün, Ö., An Integrated Multiobjective Decision Making Process for Supplier Selection and Order Allocation (2008) Omega, 36 (1), pp. 76-90; Ding, H., Benyoucef, L., Xie, X., Stochastic Multi-objective Production-distribution Network Design Using Simulation-Based Optimization (2009) International Journal of Production Research, 47 (2), pp. 479-505; Dotoli, M., Falagario, M., A Hierarchical Model for Optimal Supplier Selection in Multiple Sourcing Contexts (2012) International Journal of Production Research, 50 (11), pp. 2953-2967; Dotoli, M., Fanti, M.P., Meloni, C., Zhou, M.C., A Multi-level Approach for Network Design of Integrated Supply Chains (2005) International Journal of Production Research, 43 (20), pp. 4267-4287; Dotoli, M., Epicoco, N., Falagario, M., Sciancalepore, F., A Cross-efficiency Fuzzy Data Envelopment Analysis Technique for Performance Evaluation of Decision Making Units under Uncertainty (2015) Computers and Industrial Engineering, 79, pp. 103-114; Dotoli, M., Epicoco, N., Falagario, M., A Technique for Supply Chain Network Design under Uncertainty using Cross-efficiency Fuzzy Data Envelopment Analysis (2015) Proceedings of 15th IFAC Symposium on Information Control in Manufacturing (INCOM 2015), 48, pp. 634-639. , Ottawa (Canada), :; Dotoli, M., Epicoco, N., Falagario, M., Sciancalepore, F., A Stochastic Cross-efficiency Data Envelopment Analysis Approach for Supplier Selection under Uncertainty (2016) International Transactions in Operational Research, 23 (4), pp. 725-748; Doyle, J., Green, R., Efficiency and Cross-efficiency in DEA: Derivations, Meanings and Uses (1994) Journal of the Operational Research Society, 45, pp. 567-578; Dubey, R., Gunasekaran, A., Childe, S.J., The Design of a Responsive Sustainable Supply Chain Network under Uncertainty (2015) The International Journal of Advanced Manufacturing Technology, 80, pp. 427-445; Farahani, R.Z., Rezapour, S., Drezner, T., Fallah, S., Competitive Supply Chain Network Design: An Overview of Classifications, Models, Solution Techniques and Applications (2014) Omega, 45, pp. 92-118; Firoozi, M., Siadat, A., Salehi, N., Mousavi, S.M., A Novel Multi-objective Fuzzy Mathematical Model for Designing a Sustainable Supply Chain Network Considering Outsourcing Risk under Uncertainty (2013) Proceedings of 2013 IEEE International Conference on Industrial Engineering and Engineering Management, , Bangkok:; (2016), https://www.gnu.org/software/glpk/; Hatami-Marbini, A., Emrouznejad, A., Tavana, M., A Taxonomy and Review of the Fuzzy Data Envelopment Analysis Literature: Two Decades in the Making (2011) European Journal of Operational Research, 214 (3), pp. 457-472; Ho, W., Xu, X., Dey, P.K., Multi-criteria Decision Making Approaches for Supplier Evaluation and Selection: A Literature Review (2010) European Journal of Operational Research, 202 (1), pp. 16-24; Huang, S.Y., Teghem, J., (2012) Stochastic versus Fuzzy Approaches to Multiobjective Mathematical Programming under Uncertainty, , Springer Science & Business Media, Dordrecht, The Netherlands:; Jiménez, M., Bilbao, A., Pareto-optimal Solutions in Fuzzy Multi-objective Linear Programming (2009) Fuzzy Sets and Systems, 160, pp. 2714-2721; Kannan, D., Khodaverdi, R., Olfat, L., Jafarian, A., Diabat, A., Integrated Fuzzy Multi Criteria Decision Making Method and Multi-objective Programming Approach for Supplier Selection and Order Allocation in a Green Supply Chain (2013) Journal of Cleaner Production, 47, pp. 355-367; Kao, C., Efficiency Decomposition in Network Data Envelopment Analysis: A Relational Model (2009) European Journal of Operational Research, 192, pp. 949-962; Kao, C., Emrouznejad, A., Tavana, M., Network Data Envelopment Analysis with Fuzzy Data (2014) Performance Measurement with Fuzzy Data Envelopment Analysis. Studies in Fuzziness and Soft Computing, pp. 191-206. , Verlag: Springer-Verlag Berlin Heidelberg; Klibi, W., Martel, A., The Design of Robust Value-creating Supply Chain Networks (2013) OR Spectrum, 35, pp. 867-903; Kristianto, Y., Gunasekaran, A., Helo, P., Hao, Y., A Model of Resilient Supply Chain Network Design: A Two-stage Programming with Fuzzy Shortest Path (2014) Expert Systems with Applications, 41, pp. 39-49; Lee, J.H., Moon, I.-K., Park, J.-H., Multi-level Supply Chain Network Design with Routing (2010) International Journal of Production Research, 48 (13), pp. 3957-3976; Li, Z., Wong, W.K., Kwong, C.K., An Integrated Model of Material Supplier Selection and Order Allocation Using Fuzzy Extended AHP and Multi-objective Programming (2013) Mathematical Problems in Engineering, p. 14 pages; Liang, T.-F., Application of Fuzzy Sets to Manufacturing/Distribution Planning Decisions in Supply Chains (2011) Information Sciences, 181, pp. 842-854; Liang, L., Feng, Y., Wade, D.C., Joe, Z., DEA Models for Supply Chain Efficiency Evaluation (2006) Annals of Operations Research, 145, pp. 35-49; Lozano, S., Moreno, P., Emrouznejad, A., Tavana, M., Network Fuzzy Data Envelopment Analysis (2014) Performance Measurement with Fuzzy Data Envelopment Analysis. Studies in Fuzziness and Soft Computing, 309, pp. 207-230; Maheshwari, S., Jain, P.K., Katalinic, B., Supply Chain Management–Review on Risk Management from Supplier’s Perspective (2014) DAAAM International Scientific Book, 44, pp. 557-566; Mahnam, M., Yadollahpour, M.R., Famil-Dardashti, V., Hejazi, S.R., Supply Chain Modeling in Uncertain Environment with Bi-objective Approach (2009) Computers & Industrial Engineering, 56 (4), pp. 1535-1544; Matinrad, N., Roghanian, E., Rasi, Z., Supply Chain Network Optimization: A Review of Classification, Models, Solution Techniques and Future Research (2013) Uncertain Supply Chain Management, 1, pp. 1-24; Mirzapour Al-e-hashem, S.M.J., Maleklyand, H., Aryanezhad, M.B., A Multi-objective Robust Optimization Model for Multi-product Multi-site Aggregate Production Planning in a Supply Chain under Uncertainty (2011) International Journal of Production Economics, 134, pp. 28-42; Moghaddam, K.S., Supplier Selection and Order Allocation in Closed-loop Supply Chain Systems Using Hybrid Monte Carlo Simulation and Goal Programming (2015) International Journal of Production Research, 53 (20), pp. 6320-6338; Nagurney, A., Optimal Supply Chain Network Design and Redesign at Minimal Total Cost and with Demand Satisfaction (2010) International Journal of Production Economics, 128 (1), pp. 200-208; Pagell, M., Wiengarten, F., Fynes, B., Institutional Effects and the Decision to Make Environmental Investments (2013) International Journal of Production Research, 51 (2), pp. 427-446; Paksoy, T., Özceylan, E., Weber, G.W., Profit Oriented Supply Chain Network Optimization (2013) Central European Journal of Operations Research, 21, pp. 455-478; Parthiban, P., Zubar, H.A., Katakar, P., Vendor Selection Problem: A Multi-criteria Approach Based on Strategic Decisions (2013) International Journal of Production Research, 51 (5), pp. 1535-1548; Peidro, D., Mula, J., Poler, R., Verdegay, J., Fuzzy Optimization for Supply Chain Planning under Supply, Demand and Process Uncertainties (2009) Fuzzy Sets and Systems, 160 (18), pp. 2640-2657; Pfohl, H.-C., Köhler, H., Thomas, D., State of the Art in Supply Chain Risk Management Research: Empirical and Conceptual Findings and a Roadmap for the Implementation in Practice (2010) Logistics Research, 2, pp. 33-44; Pishvaee, M.S., Torabi, S.A., A Possibilistic Programming Approach for Closed-loop Supply Chain Network Design under Uncertainty (2010) Fuzzy Sets and Systems, 161 (20), pp. 2668-2683; Rommelfanger, H., A General Concept for Solving Linear Multicriteria Programming Problems with Crisp, Fuzzy or Stochastic Values (2007) Fuzzy Sets and Systems, 158, pp. 1892-1904; Sawik, T., Integrated Selection of Suppliers and Scheduling of Customer Orders in the Presence of Supply Chain Disruption Risks (2013) International Journal of Production Research, 51 (23-24), pp. 7006-7022; Sexton, T.R., Silkman, R.H., Hogan, A.J., Data Envelopment Analysis: Critique and Extensions (1986) Measuring Efficiency: An Assessment of Data Envelopment Analysis, pp. 73-105. , Silkman R.H., (ed), San Francisco, CA: Jossey-Bass; Sha, D.Y., Che, Z.H., Supply Chain Network Design: Partner Selection and Production/Distribution Planning Using a Systematic Model (2006) Journal of the Operational Research Society, 57, pp. 52-62; Shen, Z.J., Integrated Supply Chain Design Models: A Survey and Future Research Directions (2007) Journal of Industrial and Management Optimization, 3 (1), pp. 1-27; Tavana, M., Mirzagoltabar, H., Mirhedayatian, S.M., Saen, R.F., Azadi, M., A New Network Epsilon-Based DEA Model for Supply Chain Performance Evaluation (2013) Computers & Industrial Engineering, 66, pp. 501-513; Ting, S.C., Cho, D.I., An Integrated Approach for Supplier Selection and Purchasing Decisions (2008) Supply Chain Management: An International Journal, 13 (2), pp. 116-127; Torabi, S.A., Hassini, E., An Interactive Possibilistic Programming Approach for Multiple Objective Supply Chain Master Planning (2008) Fuzzy Sets and Systems, 159 (2), pp. 193-214; Troutt, M.D., Ambrose, P.J., Chan, C.K., Multi-stage Efficiency Tools for Goal Setting and Monitoring in Supply Chains (2004) Successful Strategies in Supply Chain Management, pp. 28-49. , Chan C.K., Lee H.W.J., (eds), Hershey: Idea Group Publishing Co; Venkatadri, U., Srinivasan, A., Montreuil, B., Saraswat, A., Optimization-based Decision Support for Order Promising in Supply Chain Networks (2006) International Journal of Production Economics, 103 (1), pp. 117-130; Wang, F., Lai, X., Shi, N., A Multi-objective Optimization for Green Supply Chain Network Design (2011) Decision Support Systems, 51, pp. 262-269; Wu, C., Barnes, D., A Literature Review of Decision-making Models and Approaches for Partner Selection in Agile Supply Chains (2011) Journal of Purchasing and Supply Management, 17, pp. 256-274; Wu, C., Barnes, D., Rosenberg, D., Luo, X., An Analytic Network Process-mixed Integer Multiobjective Programming Model for Partner Selection in Agile Supply Chains (2009) Production Planning & Control, 20 (3), pp. 254-275; Xia, W., Wu, Z., Supplier Selection with Multiple Criteria in Volume Discount Environments (2007) Omega, 35 (5), pp. 494-504; Yang, G., Wang, Z., Li, X., The Optimization of the Closed-loop Supply Chain Network (2009) Transportation Research Part E: Logistics and Transportation Review, 45 (1), pp. 16-28; Yang, F., Wu, D., Liang, L., Bi, G., Wu, D.D., Supply Chain DEA: Production Possibility Set And Performance Evaluation Model (2011) Annals of Operations Research, 185, pp. 195-211; Zimmermann, H.J., (2001) Fuzzy Set Theory and Its Applications, , 4th ed., Boston: Kluwer Academic}, document_type={Article}, source={Scopus}, }`

- Carli, R., Dotoli, M. & Pellegrino, R. (2017) A Hierarchical Decision-Making Strategy for the Energy Management of Smart Cities. IN IEEE Transactions on Automation Science and Engineering, 14.505-523.

[Bibtex]`@ARTICLE{Carli2017505, author={Carli, R. and Dotoli, M. and Pellegrino, R.}, title={A Hierarchical Decision-Making Strategy for the Energy Management of Smart Cities}, journal={IEEE Transactions on Automation Science and Engineering}, year={2017}, volume={14}, number={2}, pages={505-523}, doi={10.1109/TASE.2016.2593101}, note={cited By 44}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85027724511&doi=10.1109%2fTASE.2016.2593101&partnerID=40&md5=b6d44e7085576476833b789dae66c70a}, abstract={This paper presents a hierarchical decision-making strategy for the energy management of a smart city. The proposed decision process supports the city energy manager and local policy makers in taking energy retrofit decisions on different urban sectors by an integrated, structured, and transparent management. To this aim, in the proposed decision strategy, a bilevel programming model integrates several local decision-making units, each focusing on the energy retrofit optimization of a specific urban subsystem, and a central decision unit. We solve the hierarchical decision problem by a game theoretic distributed algorithm. We apply the developed decision model to the case study of the city of Bari (Italy), where a smart city program has recently been launched. © 2015 IEEE.}, author_keywords={Bilevel programming; energy efficiency; energy management; game theory; hierarchical decision making; optimization; smart city}, keywords={Decision theory; Energy efficiency; Energy management; Game theory; Optimization; Retrofitting; Smart city, Bi-level programming; Bilevel programming models; Decision modeling; Decision process; Decision strategy; Energy retrofit; Hierarchical decisions; Local decision-making, Decision making}, references={(2015) World Population Prospects: The 2015 Revision, Key Findings and Advance Tables, , document ESA/P/WP.241, United Nations, Dept. Econ. Social Affairs, Population Division; Abdoullaev, A., A smart world: A development model for intelligent cities (2011) Proc. 11th IEEE Int. Conf. Comput. Inf. Technol., pp. 1-28. , Aug. 31-Sep. 2; (2011) Cities and Climate Change: Policy Directions, Global Report on Human Settlement 2011, , UN-HABITAT, Earthscan, London, U.K; (2014) The World Bank Annual Report 2014, , https://openknowledge.worldbank.org/handle/10986/20093, Washington, DC, USA. [Online]; Batty, M., Smart cities of the future (2012) Eur. Phys. J. Special Topics, 214 (1), pp. 481-518; Albino, V., Berardi, U., Dangelico, R.M., Smart cities: Definitions, dimensions, performance, and initiatives (2015) J. Urban Technol., 22 (1), pp. 3-21; Belissent, J., (2011) The Core of A Smart City Must Be Smart Governance, , Cambridge, MA, USA: Forrester Research, Inc; Carli, R., Deidda, P., Dotoli, M., Pellegrino, R., An urban control center for the energy governance of a smart city (2014) Proc. IEEE ETFA, pp. 1-7. , Sep; Carli, R., Dotoli, M., Pellegrino, R., Ranieri, L., A decision making technique to optimize a buildings' stock energy efficiency IEEE Trans. Syst., Man, Cybern., Syst., , to be published; Carli, R., Dotoli, M., Pellegrino, R., ICT and optimization for the energy management of smart cities: The street lighting decision panel (2015) Proc. IEEE ETFA, pp. 1-6. , Sep; Carli, R., Dotoli, M., Pellegrino, R., Ranieri, L., Measuring and managing the smartness of cities: A framework for classifying performance indicators (2013) Proc. IEEE SM, pp. 1288-1293. , Oct; Sun, B., Luh, P.B., Jia, Q.-S., Jiang, Z., Wang, F., Song, C., Building energy management: Integrated control of active and passive heating, cooling, lighting, shading, and ventilation systems (2013) IEEE Trans. Autom. Sci. Eng., 10 (3), pp. 588-602. , Jul; Caccavelli, D., Gugerli, H., TOBUS-A European diagnosis and decision-making tool for office building upgrading (2002) Energy Buildings, 34 (2), pp. 113-119; Diakaki, C., Grigoroudis, E., Kabelis, N., Kolokotsa, D., Kalaitzakis, K., Stavrakakis, G., A multi-objective decision model for the improvement of energy efficiency in buildings (2010) Energy, 35 (12), pp. 5483-5496; Roulet, C.-A., ORME: A multicriteria rating methodology for buildings (2002) Building Environ., 37 (6), pp. 579-586. , Jun; Dall'O, G., Norese, M.F., Galante, A., Novello, C., A multicriteria methodology to support public administration decision making concerning sustainable energy action plans (2013) Energies, 6 (8), pp. 4308-4330; Ku, K.L., Liaw, J.S., Tsai, M.Y., Liu, T.S., Automatic control system for thermal comfort based on predicted mean vote and energy saving (2015) IEEE Trans. Autom. Sci. Eng., 12 (1), pp. 378-383. , Jan; Lu, C.-H., Energy-responsive aggregate context for energy saving in a multi-resident environment (2014) IEEE Trans. Autom. Sci. Eng., 11 (3), pp. 715-729. , Jul; Sun, B., Luh, P.B., Jia, Q.-S., O'Neill, Z., Song, F., Building energy doctors: An SPC and Kalman filter-based method for system-level fault detection in HVAC systems (2014) IEEE Trans. Autom. Sci. Eng., 11 (1), pp. 215-229. , Jan; Sun, B., Luh, P.B., Jia, Q.-S., Yan, B., Event-based optimization within the Lagrangian relaxation framework for energy savings in HVAC systems (2015) IEEE Trans. Autom. Sci. Eng., 12 (4), pp. 1396-1406. , Oct; Xu, Z., Jia, Q.-S., Guan, X., Supply demand coordination for building energy saving: Explore the soft comfort (2015) IEEE Trans. Autom. Sci. Eng., 12 (2), pp. 656-665. , Apr; Huang, H.-Y., Yen, J.-Y., Chen, S.-L., Ou, F.-C., Development of an intelligent energy management network for building automation (2004) IEEE Trans. Autom. Sci. Eng., 1 (1), pp. 14-25. , Jul; Covitti, A., Delvecchio, G., Neri, F., Ripoli, A., Labini, M.S., Road lighting installation design to optimize energy use by genetic algorithms (2005) Proc. Int. Conf. EUROCON, 2, pp. 1541-1544. , Nov; Gómez-Lorente, D., Rabaza, O., Espín, A., Peña-García, A., Optimization of efficiency and energy saving in public lighting with multi-objective evolutionary algorithms (2013) Proc. ICREPQ, pp. 1-4. , Mar; Huang, S.-C., Lee, L.-L., Jeng, M.-S., Hsieh, Y.-C., Assessment of energy-efficient LED street lighting through large-scale demonstration (2012) Proc. Int. Conf. Renew. Energy Res. Appl., pp. 1-5. , Nov; Burgos-Payán, M., Correa-Moreno, F.-J., Riquelme-Santos, J.-M., Improving the energy efficiency of street lighting. A case in the South of Spain (2012) Proc. Int. Conf. Eur. Energy Market, pp. 1-8. , May; Coutinho-Rodrigues, J., Simão, A., Antunes, C.H., A GISbased multicriteria spatial decision support system for planning urban infrastructures (2011) Decision Support Syst., 51 (3), pp. 720-726. , Jun; Jajac, N., Knezic, S., Marovic, I., Decision support system to urban infrastructure maintenance management (2009) Org., Technol. Manage. Construction, Int. J., 1 (2), pp. 72-79; Liu, J., Li, J., A bi-level energy-saving dispatch in smart grid considering interaction between generation and load (2015) IEEE Trans. Smart Grids, 6 (3), pp. 1443-1452. , May; Ocalir-Akunal, E.V., (2015) Using Decision Support Systems for Transportation Planning Efficiency, , Hershey, PA, USA: IGI Global; Lam, A.Y.S., Leung, Y.-W., Chu, X., Electric vehicle charging station placement: Formulation, complexity, and solutions (2014) IEEE Trans. Smart Grids, 5 (6), pp. 2846-2856. , Nov; Domínguez, M., Fernández-Cardador, A., Cucala, A.P., Pecharromán, R.R., Energy savings in metropolitan railway substations through regenerative energy recovery and optimal design of ATO speed profiles (2012) IEEE Trans. Autom. Sci. Eng., 9 (3), pp. 496-504. , Jul; Caponio, G., D'Alessandro, G., Digiesi, S., Mossa, G., Mummolo, G., Verriello, R., Minimizing carbon-footprint of municipal waste separate collection systems (2015) Enhancing Synergies in A Collaborative Environment, pp. 351-359. , Switzerland: Springer International Publishing; Makropoulos, C.K., Butler, D., Maksimovic, C., Fuzzy logic spatial decision support system for urban water management (2003) J. Water Resour. Planning Manage, 129 (1), pp. 69-77. , Jan; Thomas, M.R., A GIS-based decision support system for brownfield redevelopment (2002) Landscape Urban Planning, 58 (1), pp. 7-23; Wang, H., Zhang, X., Skitmore, M., Implications for sustainable land use in high-density cities: Evidence from Hong Kong (2015) Habitat Int., 50, pp. 23-34. , Dec; Naphade, M., Banavar, G., Harrison, C., Paraszczak, J., Morris, R., Smarter cities and their innovation challenges (2011) Computer, 44 (6), pp. 32-39. , Jun; Calvillo, C.F., Sánchez-Miralles, A., Villar, J., Energy management and planning in smart cities (2016) Renew. Sustain. Energy Rev., 55, pp. 273-287. , Mar; Bartlett, D., Harthoorn, W., Hogan, J., Kehoe, M., Schloss, R.J., Enabling integrated city operations (2011) IBM J. Res. Develop., 55 (1-2), pp. 1-10. , Jan./Mar; Mattoni, B., Gugliermetti, F., Bisegna, F., A multilevel method to assess and design the renovation and integration of smart cities (2015) Sustain. Cities Soc., 15, pp. 105-119. , Jul; Suakanto, S., Supangkat, S.H., Suhardi, Saragih, R., Smart city dashboard for integrating various data of sensor networks (2013) Proc. IEEE Int. Conf. ICT Smart Soc. (ICISS), pp. 1-5. , Jun; Adepetu, A., Grogan, P., Alfaris, A., Svetinovic, D., De Weck, O., City. Net IES: A sustainability-oriented energy decision support system (2012) Proc. IEEE SysCon, pp. 1-7. , Mar; Yamagata, Y., Seya, H., Simulating a future smart city: An integrated land use-energy model (2013) Appl. Energy, 112, pp. 1466-1474. , Dec; Kim, S.A., Shin, D., Choe, Y., Seibert, T., Walz, S.P., Integrated energy monitoring and visualization system for smart green city development: Designing a spatial information integrated energy monitoring model in the context of massive data management on a Web based platform (2012) Autom. Construction, 22, pp. 51-59. , Mar; Phdungsilp, A., Integrated energy and carbon modeling with a decision support system: Policy scenarios for low-carbon city development in Bangkok (2010) Energy Policy, 38 (9), pp. 4808-4817; Juan, Y.-K., Wang, L., Wang, J., Leckie, J.O., Li, K.-M., A decisionsupport system for smarter city planning and management (2011) IBM J. Res. Develop., 55 (1-2), pp. 30-41; Gironès, V.C., Moret, S., Maréchal, F., Favrat, D., Strategic energy planning for large-scale energy systems: A modelling framework to aid decision-making (2015) Energy, 90, pp. 173-186. , Oct; Carli, R., Albino, V., Dotoli, M., Mummolo, G., Savino, M., A dashboard and decision support tool for the energy governance of smart cities (2015) Proc. IEEE EESM, pp. 23-28. , Jul; Kornai, J., Liptak, T., Two-level planning (1965) Econometrica, J. Econ. Soc., 33 (1), pp. 141-169; Vicente, L.N., Calamai, P.H., Bilevel and multilevel programming: A bibliography review (1994) J. Global Optim., 5 (3), pp. 291-306; Kolokotsa, D., Diakaki, C., Grigoroudis, E., Stavrakakis, G., Kalaitzakis, K., Decision support methodologies on the energy efficiency and energy management in buildings (2009) Adv. Building Energy Res., 3 (1), pp. 121-146; Martello, S., Toth, P., (1990) Knapsack Problems: Algorithms and Computer Implementations, , New York, NY, USA: Wiley; Bertsekas, D.P., (1999) Nonlinear Programming, p. 780. , Belmont, MA, USA: Athena Scientific; Dempe, S., (2002) Foundations of Bilevel Programming, , Dordrecht, The Netherlands: Kluwer; Gümüs, Z.H., Floudas, C.A., Global optimization of mixed-integer bilevel programming problems (2005) Comput. Manage. Sci., 2 (3), pp. 181-212; Vicente, L., Savard, G., Judice, J., Discrete linear bilevel programming problem (1996) J. Optim. Theory Appl., 89 (3), pp. 597-614; Colson, B., Marcotte, P., Savard, G., An overview of bilevel optimization (2007) Ann. Oper. Res., 153 (1), pp. 235-256. , Sep; Dempe, S., Richter, K., Bilevel programming with knapsack constraints (2000) Central Eur. J. Oper. Res., 8 (2), pp. 93-107; Fanghänel, D., Dempe, S., Bilevel programming with discrete lower level problems (2009) Optim., J. Math. Program. Oper. Res., 58 (8), pp. 1029-1047; Basar, T., Olsder, G.J., (1999) Dynamic Noncooperative Game Theory, , (Classics in Applied Mathematics). Philadelphia, PA, USA: SIAM; Simaan, M., Cruz, J.B., A Stackelberg solution for games with many players (1973) Proc. Joint Autom. Control Conf., pp. 344-347; Boyd, S., Vandenberghe, L., (2004) Convex Optimization, , Cambridge, U.K.: Cambridge Univ. Press; Fisk, C.S., Game theory and transportation systems modelling (1984) Transp. Res. B, Methodol., 18 (4-5), pp. 301-313; Bertsekas, D.P., Tsitsiklis, J.N., (1989) Parallel and Distributed Computation: Numerical Methods, 23. , Englewood Cliffs, NJ, USA: Prentice-Hall; Bhowmick, A., (2012) IBM Intelligent Operations Center for Smarter Cities Administration Guide, , IBM Redbooks; (2016) MATLAB Engine Documentation, , http://it.mathworks.com/help/matlab/matlab_external/introducing-matlab-engine.html, accessed on Mar. 18, [Online]; Achterberg, T., SCIP: Solving constraint integer programs (2009) Math. Program. Comput., 1 (1), pp. 1-41; (2016) OPTI: A Free MATLAB Toolbox for Optimization, , http://www.i2c2.aut.ac.nz/Wiki/OPTI/, accessed on Mar. 18, [Online]; (2016) Covenant Official Text, , http://www.covenantofmayors.eu/, accessed on Mar. 18, [Online]; Cassidy, R.G., Kirby, M.J.L., Raike, W.M., Efficient distribution of resources through three levels of government (1971) Manage. Sci., 17 (8), pp. B462-B473}, document_type={Article}, source={Scopus}, }`

- Dotoli, M., Grammatico, S. & Ciulli, N. (2017) Guest Editorial Special Issue on Automation and Optimization for Energy Systems. IN IEEE Transactions on Automation Science and Engineering, 14.410-413.

[Bibtex]`@ARTICLE{Dotoli2017410, author={Dotoli, M. and Grammatico, S. and Ciulli, N.}, title={Guest Editorial Special Issue on Automation and Optimization for Energy Systems}, journal={IEEE Transactions on Automation Science and Engineering}, year={2017}, volume={14}, number={2}, pages={410-413}, doi={10.1109/TASE.2017.2670758}, art_number={7870619}, note={cited By 1}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85014814197&doi=10.1109%2fTASE.2017.2670758&partnerID=40&md5=553e7762a73691b7675d0cc34dfa8620}, keywords={Electrical engineering, Energy systems; Special sections, Automation}, document_type={Editorial}, source={Scopus}, }`

- Dotoli, M., Fay, A., Miśkowicz, M. & Seatzu, C. (2017) Advanced control in factory automation: a survey. IN International Journal of Production Research, 55.1243-1259.

[Bibtex]`@ARTICLE{Dotoli20171243, author={Dotoli, M. and Fay, A. and Miśkowicz, M. and Seatzu, C.}, title={Advanced control in factory automation: a survey}, journal={International Journal of Production Research}, year={2017}, volume={55}, number={5}, pages={1243-1259}, doi={10.1080/00207543.2016.1173259}, note={cited By 19}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84963805196&doi=10.1080%2f00207543.2016.1173259&partnerID=40&md5=ea6c662282f1e090c503fff3e31d8e63}, abstract={This paper provides a survey of the main advanced control techniques currently adopted in factory automation. In particular, it focuses on five classes of control approaches, namely: model-based control, control based on computational intelligence, adaptive control, discrete event systems-based control and finally event-triggered and self-triggered control. A particular focus is put on the most significant and recent contributions in these areas with attention to their application in the factory automation domain. Finally, open issues, challenges and the requirements of further research efforts for each class are pointed out. © 2016 Informa UK Limited, trading as Taylor & Francis Group.}, author_keywords={adaptive control; advanced control; control based on computational intelligence; discrete event systems control; event-triggered control; factory automation; model predictive control; model-based control}, keywords={Artificial intelligence; Automation; Discrete event simulation; Factory automation; Model predictive control; Surveys, Adaptive Control; Advanced control; Control approach; Event-triggered; Event-triggered controls; Model based controls; Research efforts; Self-triggered controls, Adaptive control systems}, references={Albertos, P., Sala, A., (2004) Multivariable Control Systems: An Engineering Approach, , London: Springer-Verlag; Alur, R., Dill, D.L., A Theory of Timed Automata (1994) Theoretical Computer Science, 126, pp. 183-235; Anta, A., Tabuada, P., To Sample or Not to Sample: Self-triggered Control for Nonlinear Systems (2010) IEEE Transactions on Automatic Control, 55 (9), pp. 2030-2042; Araujo, J., Mazo, M., Jr., Anta, A., Tabuada, P., Johansson, K.H., System Architectures, Protocols and Algorithms for Aperiodic Wireless Control Systems (2014) IEEE Transactions on Industrial Informatics, 10 (1), pp. 175-184; Åstrom, K.J., Kumar, P.R., Control: A perspective (2014) Automatica, 50 (1), pp. 3-43; Åstrom, K.J., Wittenmark, B., (1995) Adaptive Control, , Reading, MA: Addison-Wesley; Åstrom, K.J., (2008) Analysis and Design of Nonlinear Control Systems, pp. 127-147. , Astolfi A., Marconi L., (eds), Berlin: Springer; Balduzzi, F., Giua, A., Seatzu, C., Modeling and Simulation of Manufacturing Systems with First-order Hybrid Petri Nets (2001) International Journal of Production Research, 39, pp. 255-282; Blevins, T.L., (2012) Proceedings IFAC Conference on Advances in PID Control, 2. , Brescia, Italy:; Blevins, T., Nixon, M., Wojsznis, W., Event Based Control Applied to Wireless Throttling Valves (2015) Proceedings of International Conference on Event-Based Control, Commmunication, and Signal Processing, , Krakow, Poland:; Boisseau, B., Durand, S., Martinez-Molina, J.J., Raharijaona, T., Marchand, N., Attitude Control of a Gyroscope Actuator Using Event-based Discrete-time Approach (2015) Proceedings of International Conference on Event-Based Control, Communication, and Signal Processing, , Krakow, Poland:; Camacho, E.F., Alba Bordons, C., (2007) Model Predictive Control, , London: Springer-Verlag; Camacho, E.F., Ramirez, D.R., Limon, D., Muñoz de la Peña, D., Alamo, T., Model Predictive Control Techniques for Hybrid Systems (2010) Annual Reviews in Control, 34 (1), pp. 21-31; Campos, J., Seatzu, C., Xie, X., (2014) Formal Methods in Manufacturing, , Boca Raton, FL: CRC Press; Cassandras, C.G., Lafortune, S., (2008) Introduction to Discrete Event Systems, , 2nd ed, Boston, MA: Kluwer Academic; Cassandras, C.G., Event-Driven Control and Optimization in Hybrid System (2016) Event-based Control and Signal Processing, pp. 21-36. , Miśkowicz M., (ed), Boca Raton, FL: CRC Press; Cassandras, C.G., Wardi, Y., Melamed, B., Sun, G., Panayiotou, C.G., Perturbation Analysis for On-line Control and Optimization of Stochastic Fluid Models (2002) IEEE Transactions on Automatic Control, 47 (8), pp. 1234-1248; Castelnuovo, A., Ferrarini, L., Piroddi, L., An Incremental Petri Net-Based Approach to the Modeling of Production Sequences in Manufacturing Systems (2007) IEEE Transactions on Automation Science and Engineering, 4, pp. 424-434; Chan, K.Y., Kwong, C.K., Tsim, Y.C., A Genetic Programming Based Fuzzy Regression Approach to Modelling Manufacturing Processes (2010) International Journal of Production Research, 48 (7), pp. 1967-1982; Christofides, P.D., Scattolini, R., Muñoz de la Peña, D., Liu, J., Distributed Model Predictive Control: A Tutorial Review and Future Research Directions (2013) Computers & Chemical Engineering, 51, pp. 21-41; Conte, G., Perdon, A.M., Vitaioli, G., (2009) Proceedings of Mediterranean Conference on Control & Automation, , Thessaloniki, Greece:; Cordone, R., Nazeem, A., Piroddi, L., Reveliotis, S., Designing Optimal Deadlock Avoidance Policies for Sequential Resource Allocation Systems Through Classification Theory: Existence Results and Customized Algorithms (2013) IEEE Transactions on Automatic Control, 58 (11), pp. 2713-2728; Craig, I., Control in the Process Industries (2011) The Impact of Control Technology, , http://www.ieeecss.org, Samad T., Annaswamy A.M., (eds), IEEE Control Systems Society; Darby, M.L., Harmse, M., Nikolaou, M., MPC: Current Practice and Challenges (2012) Control Engineering Practice, 20 (4), pp. 328-342; David, R., Alla, H., (2005) Discrete, Continuous and Hybrid Petri nets, , Berlin: Springer; Demongodin, I., Generalized Batches Petri Net: Hybrid Model for High Speed Systems with Variable Delays (2001) Discrete Event Dynamic Systems: Theory and Applications, 11 (1-2), pp. 137-162; Dereli, T., Baykasoglu, A., Altun, K., Durmusoglu, A., Türksen, I.B., Industrial Applications of Type-2 Fuzzy Sets and Systems: A Concise Review (2011) Computers in Industry, 62 (2), pp. 125-137; Dotoli, M., Lino, P., Maione, B., Naso, D., Turchiano, B., Genetic Optimization of Fuzzy Sliding Mode Controllers: An Experimental Study (2003) Soft Computing Applications, pp. 193-205. , Bonarini A., Masulli F., Pasi G., (eds), Heidelberg: Physica Verlag; Dotoli, M., Fanti, M.P., Giua, A., Seatzu, C., First-order Hybrid Petri Nets. An Application to Distributed Manufacturing Systems (2008) Nonlinear Analysis: Hybrid Systems, 2 (2), pp. 408-430; Dutta, R.K., Paul, S., Chattopadhyay, A.B., Fuzzy Controlled Backpropagation Neural Network for Tool Condition Monitoring in Face Milling (2000) International Journal of Production Research, 38 (13), pp. 2989-3010; Estrada, T., Lin, H., Antsaklis, P.J., Model Based Control with Intermittent Feedback (2006) Proceedings of 14th Mediterranean Conference on Control and Automation, , Ancona, Italy:; Fan, S.K.S., Wang, C.-Y., On-line Tuning System of Multivariate dEWMA Control Based on a Neural Network Approach (2008) International Journal of Production Research, 46 (13), pp. 3459-3484; Feng, G., A Survey on Analysis and Design of Model-based Fuzzy Control Systems (2006) IEEE Transactions on Fuzzy Systems, 14 (5), pp. 676-697; Fleming, P.J., Purshouse, R.C., Evolutionary Algorithms in Control Systems Engineering: A Survey (2002) Control Engineering Practice, 10 (11), pp. 1223-1241; Garcia, E., Antsaklis, P.J., Montestruque, L.A., (2014) Model-based Control of Networked Systems, , Cham: Springer; Gawthrop, P., Gollee, H., Loram, I., Intermittent Control in Man and Machine (2016) Event-based Control and Signal Processing, pp. 281-350. , Miśkowicz M., (ed), Boca Raton, FL: CRC Press; Giua, A., Pilloni, M.T., Seatzu, C., Modelling and Simulation of a Bottling Plant Using Hybrid Petri Nets (2001) International Journal of Production Research, 43, pp. 1375-1395; Grune, L., Hirche, S., Junge, O., Koltai, P., Lehmann, D., Lunze, J., Molin, A., Event-based Control (2014) Control Theory of Digitally Networked Dynamic Systems, pp. 169-261. , Lunze J., (ed), Heidelberg: Springer; Hajebi, P., AlModarresi, S.M.T., Online Adaptive Fuzzy Logic Controller Using Neural Network for Networked Control Systems (2012) Proceedings of 14th International Conference on Advanced Communication Technology, , Phoenix Park, Korea South:; Heemels, W.P.M.H., Donkers, M.C.F., Model-based Periodic Event-triggered Control for Linear Systems (2013) Automatica, 49, pp. 698-711; Heemels, W.P.M.H., Johansson, K.H., Tabuada, P., (2012) Proceedings of IEEE Conference on Decision and Control, , Maui, Hawai:; Heemels, W.P.M.H., Postoyan, R., Donkers, M.C.F., Teel, A.R., Anta, A., Tabuada, P., Nesic, D., Periodic Event-triggered Control (2016) Event-based Control and Signal Processing, pp. 105-120. , Miśkowicz M., (ed), CRC Press; Hensel, B., Ploennigs, J., Vasyutynskyy, V., Kabitzsch, K., Event-based PID Control (2016) Event-based Control and Signal Processing, pp. 235-260. , Miśkowicz M., (ed), Boca Raton, FL: CRC Press; Heragu, S.S., Srinivasan, M., Analysis of Manufacturing Systems via Single-class, Semi-open Queuing Networks (2011) International Journal of Production Research, 49 (2), pp. 295-319; Huang, Y., Advances in Artificial Neural Networks–Methodological Development and Application (2009) Algorithms, 2 (3), pp. 973-1007; Jämsä-Jounela, S.-L., Future Trends in Process Automation (2007) Annual Reviews in Control, 31 (2), pp. 211-220; Kwong, C.K., Bai, H., Fuzzy Regression Approach to Process Modelling and Optimisation of Epoxy Dispensing (2005) International Journal of Production Research, 43 (12), pp. 2359-2375; Lafortune, S., On Decentralized and Distributed Control of Partially-observed Discrete Event Systems (2007) Advances in Control Theory and Applications, 353, pp. 171-184. , Lecture Notes in Control and Information Sciences, Berlin Heidelberg: Springer-Verlag; Landau, I.D., Lozano, R., M’Saad, M., Karimi, A., (2011) Adaptive Control: Algorithms, Analysis and Applications, , London: Springer; Ławryńczuk, M., Neural Networks in Model Predictive Control (2009) Intelligent Systems for Knowledge Management, 252, pp. 31-63. , Studies in Computational Intelligence, Heidelberg: Springer-Verlag; Lee, J.H., Model Predictive Control: Review of the Three Decades of Development (2011) International Journal of Control, Automation and Systems, 9 (3), pp. 415-424; Leith, D.J., Leithead, W.E., Survey of Gain-scheduling Analysis and Design (2000) International Journal of Control, 73 (11), pp. 1001-1025; Linkens, D.A., Nyongesa, H.O., Learning Systems in Intelligent Control: An Appraisal of Fuzzy, Neural and Genetic Algorithm Control Applications (1996) IEE Proceedings-Control Theory and Applications, 143 (4), pp. 367-386; Liu, Q., Wang, Z., He, X., Zhou, D.H., A Survey of Event-based Strategies on Control and Estimation (2014) Systems Science & Control Engineering: An Open Access Journal, 2 (1), pp. 90-97; Lunze, J., Event-based Control: Introduction and Survey (2016) Event-based Control and Signal Processing, pp. 3-20. , Miśkowicz M., (ed), Boca Raton, FL: CRC Press; Magali, R.G., Meireles, P.E.M., Simões, M.G.S., A Comprehensive Review for Industrial Applicability of Artificial Neural Networks (2003) IEEE Transactions on Industrial Electronics, 50 (3), pp. 585-601; Maione, B., Naso, D., Evolutionary Adaptation of Dispatching Agents in Heterarchical Manufacturing Systems (2001) International Journal of Production Research, 39 (7), pp. 1481-1503; Mamdani, E.H., Assilian, S., An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller (1975) International Journal of Man-Machine Studies, 7 (1), pp. 1-13; Marck, J.W., Sijs, J., (2010) Proceedings of International Conference on Sensor Technologies and Applications, , Venice, Italy:; Mayne, D.Q., Model Predictive Control: Recent Developments and Future Promise (2014) Automatica, 50, pp. 2967-2986; Mendes, J., Araújo, R., Souza, F., Adaptive Fuzzy Identification and Predictive Control for Industrial Processes (2013) Expert Systems with Applications, 40 (17), pp. 6964-6975; Miśkowicz, M., Send-on-delta Concept: An Event-based Data Reporting Strategy (2006) Sensors, 6, pp. 49-63; Miśkowicz, M., Reducing Communication by Event-triggered Sampling (2016) Event-based Control and Signal Processing, pp. 37-58. , Miśkowicz M., (ed), Boca Raton, FL: CRC Press; Miśkowicz, M., (2016) Event-based Control and Signal Processing, , Boca Raton, FL: CRC Press; Mitchell, J., Global Industrial Automation (2012) Credit Suisse Connection Series, Credit Suisse Global Equity Research, , http://docplayer.net/5631313-Global-industrial-automation.html; Montestruque, L.A., Antsaklis, P.J., On the Model-based Control of Networked Systems (2003) Automatica, 39, pp. 1837-1843; Moore, K.E., Gupta, S.M., Petri Net Models of Flexible and Automated Manufacturing Systems: A Survey (1996) International Journal of Production Research, 34 (11), pp. 3001-3035; Morari, M., (2009) Workshop in celebration of David Clarke’s contribution to MPC, , University of Oxford, Oxford:; Morel, G., Valckenaers, P., Faure, J.-M., Pereira, C.E., Diedrich, C., Manufacturing Plant Control Challenges and Issues (2007) Control Engineering Practice, 15 (11), pp. 1321-1331; Nazeem, A., Reveliotis, S., A Practical Approach for Maximally Permissive Liveness-enforcing Supervision of Complex Resource Allocation Systems (2011) IEEE Transactions on Automation Science and Engineering, 8, pp. 766-779; Nowzari, C., Cortés, J., Self-triggered and Team-triggered Control of Networked Cyber-physical System (2016) Event-based Control and Signal Processing, pp. 203-220. , Miśkowicz M., (ed), Boca Raton, FL: CRC Press; Pathak, K.B., Adhyaru, D.M., (2012) 2012 Proceedings of NIRMA University International Conference on Engineering, , Ahmedabad, India:; Pawlowski, A., Guzman, J.L., Berenguel, M., Normey-Rico, J.E., Dormido, S., Event-based GPC for Multivariable Processes (2015) Proceedings of International Conference on Event-Based Control, Communication, and Signal Processing, , Krakow, Poland:; Pawlowski, A., Guzmán, J.L., Berenguel, M., Dormido, S., Event-based Generalized Predictive Control (2016) Event-based Control and Signal Processing, pp. 151-176. , Miśkowicz M., (ed), Boca Raton, FL: CRC Press; Pham, D.T., Castellani, M., Fuzzy Control of a System for Assembling Fiber Optic Transmitters (2006) International Journal of Production Research, 44 (17), pp. 3553-3571; Ploennigs, J., Vasyutynskyy, V., Kabitzsch, K., Comparative Study of Energy-efficient Sampling Approaches for Wireless Control Networks (2010) IEEE Transactions on Industrial Informatics, 6 (3), pp. 416-424; Popescu, C., Cavia Soto, M., Martinez Lastra, J.L., A Petri Net-based Approach to Incremental Modelling of Flow and Resources in Service-oriented Manufacturing Systems (2012) International Journal of Production Research, 50 (2), pp. 325-343; Precup, R.-E., Hellendoorn, H., A Survey on Industrial Applications of Fuzzy Control (2011) Computers in Industry, 62 (3), pp. 213-226; Qin, J., Badgwell, T.A., A survey of industrial model predictive control technology (2003) Control Engineering Practice, 11 (7), pp. 733-764; Rewagad, R.R., Kiss, A.A., Dynamic Optimization of a Dividing-wall Column Using Model Predictive Control (2012) Chemical Engineering Science, 68, pp. 2737-2764; Seatzu, C., Silva, M., van Schuppen, J.H., (2012) Control of Discrete-event Systems: Automata and Petri Net Perspectives”, 433. , Lecture Notes in Control and Information Science, London: Springer-Verlag; Shi, D., Shi, L., Chen, T., (2016) Event-based State Estimation, , A Stochastic Perspective. Berlin: Springer; Shin, Y.C., Vishnupad, P., Neuro-fuzzy Control of Complex Manufacturing Processes (1996) International Journal of Production Research, 34 (12), pp. 3291-3309; Sijs, J., Noack, B., Lazar, M., Hanebeck, U.D., Time-periodic State Estimation with Event-based Measurement Updates (2016) Event-based Control and Signal Processing, pp. 37-58. , Miśkowicz M., (ed), Boca Raton, FL: CRC Press; Silva, M., Recalde, L., On Fluidification of Petri Net Models: From Discrete to Hybrid and Continuous Models (2004) Annual Reviews in Control, 28 (2), pp. 253-266; Silva, M., Vallette, R., (1990) Petri Nets and Flexible Manufacturing, 424. , Lectures Notes in Computer Science, London: Springer-Verlag; Silva, D.B., Vieira, A.D., Loures, E.F.R., Busetti, M.A., Santos, E.A.P., Dealing with Routing in an Automated Manufacturing Cell: A Supervisory Control Theory Application (2011) International Journal of Production Research, 49 (16), pp. 4979-4998; Sivakumar, R., Manic, K.S., Nerthiga, V., Akila, R., Balu, K., Application of Fuzzy Model Predictive Control in Multivariable Control of Distillation Column (2010) International Journal of Chemical Engineering and Applications, 1 (1), pp. 38-42; Skogestad, S., Postlethwaite, I., (2005) Multivariable Feedback Control: Analysis and Design, , New York, NY: Wiley; Smith, K.D., Braun, M., Kempf, K., Lu, J., Morningred, D., Supply Chain as a Control Problem (2011) The Impact of Control Technology, , http://www.ieeecss.org/main/IoCT-report, Samad T., Annaswamy A.M., (eds), IEEE Control Systems Society,,,, and.” In, edited by; Stewart, D., Cheraghi, S.H., Malzahn, D., Fuzzy Defect Avoidance System (FDAS) for Product Defect Control (2004) International Journal of Production Research, 42 (16), pp. 3159-3182; Su, C.-T., Shiue, Y.-R., Intelligent Scheduling Controller for Shop Floor Control Systems: A Hybrid Genetic Algorithm/Decision Tree Learning Approach (2003) International Journal of Production Research, 41 (12), pp. 2619-2641; Suh, Y.S., Send-on-delta Sensor Data Transmission with a Linear Predictor (2007) Sensors, 7, pp. 537-547; Sun, Y., El-Farra, N.H., Resource Aware Quasi-decentralized Control of Networked Process Systems Over Wireless Sensor Networks (2012) Chemical Engineering Science, 69, pp. 93-106; Tallapragada, P., Chopra, N., Lyapunov Based Sampling for Adaptive Tracking Control in Robot Manipulators: An Experimental Comparison (2013) Experimental Robotics, 88, pp. 683-698. , Springer Tracts in Advanced Robotics, Berlin: Springer; Tao, G., Multivariable Adaptive Control: A Survey (2014) Automatica, 50, pp. 2737-2764; Trimpe, S., D’Andrea, R., Event-based State Estimation with Variance-based Triggering (2014) IEEE Transactions on Automatic Control, 49 (12), pp. 3266-3281; Velasco, M., Martí, P., Bini, E., On Lyapunov Sampling for Event-driven Controllers (2009) Proceedings of Joint IEEE Conference on Decision and Control and Chinese Control Conference, , Shangai, China:; Wang, X., Lemmon, M.D., Event-triggering in Distributed Networked Control Systems (2011) IEEE Transactions on Automatic Control, 56 (3), pp. 586-601; Wojsznis, W.K., Blevins, T., Nixon, M.J., Model Predictive Control with Event Driven Operation (2015) Proceedings of International Conference on Event-Based Control, Communication, and Signal Processing, , Krakow, Poland:; Zadeh, L.A., Fuzzy Logic, Neural Networks, and Soft Computing (1994) Communications of the ACM, 37 (3), pp. 77-84; Zhang, L., Rodrigues, B., Modelling Reconfigurable Manufacturing Systems with Coloured Timed Petri Nets (2009) International Journal of Production Research, 47 (16), pp. 4569-4591; Zhou, M., Fanti, M.P., (2004) Deadlock Resolution in Computer-integrated Systems, , Boca Raton, FL: CRC Press}, document_type={Article}, source={Scopus}, }`

- Dotoli, M., Epicoco, N., Falagario, M., Seatzu, C. & Turchiano, B. (2017) A decision support system for optimizing operations at intermodal railroad terminals. IN IEEE Transactions on Systems, Man, and Cybernetics: Systems, 47.487-501.

[Bibtex]`@ARTICLE{Dotoli2017487, author={Dotoli, M. and Epicoco, N. and Falagario, M. and Seatzu, C. and Turchiano, B.}, title={A decision support system for optimizing operations at intermodal railroad terminals}, journal={IEEE Transactions on Systems, Man, and Cybernetics: Systems}, year={2017}, volume={47}, number={3}, pages={487-501}, doi={10.1109/TSMC.2015.2506540}, art_number={7370812}, note={cited By 23}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85014711488&doi=10.1109%2fTSMC.2015.2506540&partnerID=40&md5=4dd2a6b2fba28746527bea33ec3dd762}, abstract={In this paper, we present a decision support tool to optimize two of the most critical activities in intermodal railroad container terminals, in an iterative and integrated framework devoted to the terminal profit improvement. First, the model allows optimizing the freight trains composition, maximizing the company profit, while respecting physical and economic constraints, and placing in the train head/tail containers prosecuting to subsequent destinations. Hence, based on the resulting train composition, the decision support system allows optimizing the containers allocation in the terminal storage yard, in order to maximize the filling level while respecting physical constraints. The model is successfully tested on a real case study, the inland railroad terminal of a leading Italian intermodal logistics company. © 2013 IEEE.}, author_keywords={Decision support system (DSS); intermodal freight transport; optimization; railroad terminal; train composition; yard container storage}, keywords={Containers; Filling; Freight transportation; Optimization; Profitability; Railroad stations; Railroad transportation; Railroad yards and terminals; Railroads; Truck terminals, Decision support system (dss); Decision support tools; Economic constraints; Integrated frameworks; Intermodal freight transport; Physical constraints; Rail-road terminals; Train composition, Decision support systems}, references={Alicke, K., Modeling, and optimization of the intermodal terminal Mega Hub (2002) OR Spectr, 24 (1), pp. 1-18; Ambrosino, D., Bramardi, A., Pucciano, M., Sacone, S., Siri, S., Modeling, and solving the train load planning problem in seaport container terminals (2011) Proc. 7th IEEE Conf. Autom. Sci. Eng., Trieste, Italy, pp. 208-213. , Aug; Arnold, P., Peeters, D., Thomas, I., Modelling a rail/road intermodal transportation system (2004) Transport. Res. e Logist. Transport. Rev, 40 (3), pp. 255-270; Ballis, A., Golias, J., Comparative evaluation of existing, and innovative rail-road freight transport terminals (2002) Transp. Res. A Policy Pract, 36 (7), pp. 593-611; Ballis, A., Golias, J., Towards the improvement of a combined transport chain performance (2004) Eur. J. Oper. Res, 152 (2), pp. 420-436; Bektas, T., Crainic, T.G., A brief overview of intermodal transportation (2007) Interuniv. Res. Centre Enterp. Netw., Logistics Transport. (CIRRELT), Université de Montréal, Montreal, QC, Canada, Tech. Rep. CIRRELT, pp. 2007-2103. , https://www.cirrelt.ca/DocumentsTravail/CIRRELT-2007-03.pdf, Online]. Available:; Bertsimas, D., Tsitsiklis, J.N., (1997) Introduction to Linear Optimization, , Belmont, MA, USA: Athena Sci; Bevilacqua, V., Costantino, N., Dotoli, M., Falagario, M., Sciancalepore, F., Strategic design, and multi-objective optimisation of distribution networks based on genetic algorithms (2012) Int. J. Comp. Integr. Manuf, 25 (12), pp. 1139-1150; Bielli, M., Boulmakoul, A., Rida, M., Object oriented model for container terminal distributed simulation (2006) Eur. J. Oper. Res, 175 (3), pp. 1731-1751; Bish, E.K., A multiple-crane-constrained scheduling problem in a container terminal (2003) Eur. J. Oper. Res, 144 (1), pp. 83-107; Bontekoning, Y.M., MacHaris, C., Trip, J.J., Is a new applied transportation research field emerging?-A review of intermodal rail-Truck freight transport literature (2004) Transp. Res. A Policy Pract, 38 (1), pp. 1-34; Bortfeldt, A., Wäscher, G., Constraints in container loading-A stateof-The-Art review (2013) Eur. J. Oper. Res, 229 (1), pp. 1-20; Boschian, V., Dotoli, M., Fanti, M.P., Iacobellis, G., Ukovich, W., A metamodeling approach to the management of intermodal transportation networks (2011) IEEE Trans. Autom. Sci. Eng, 8 (3), pp. 457-469. , Jul; Bostel, N., Dejax, P., Models, and algorithms for container allocation problems on trains in a rapid transshipment shunting yard (1998) Transport. Sci, 32 (4), pp. 370-379; Bruns, F., Knust, S., Optimized load planning of trains in intermodal transportation (2012) OR Spectr, 34 (3), pp. 511-533; Burstein, F., Holsapple, C., (2008) Handbook on Decision Support Systems, p. 798. , Heidelberg Germany: Springer; Cai, B., Multiobjective optimization for autonomous straddle carrier scheduling at automated container terminals (2013) IEEE Trans. Autom. Sci. Eng, 10 (3), pp. 711-725. , Jul; Cao, B., Uebe, G., Solving transportation problems with nonlinear side constraints with tabu search (1995) Comput. Oper. Res, 22 (6), pp. 593-603; Caris, A., MacHaris, C., Janssens, G.K., Planning problems in intermodal freight transport: Accomplishments, and prospects (2008) Transport. Plan. Technol, 31 (3), pp. 277-302; Caris, A., MacHaris, C., Janssens, G.K., Decision support in intermodal transport: A new research agenda (2013) Comput. Ind, 64 (2), pp. 105-112; Carrese, S., Tatarelli, L., Optimizing the stacking of the intermodal transport units in an inland terminal: An heuristic procedure (2011) Proc. Soc. Behav. Sci, 20, pp. 994-1003; Chen, P., Fu, Z., Lim, A., Rodrigues, B., Port yard storage optimization (2004) IEEE Trans. Autom. Sci. Eng, 1 (1), pp. 26-37. , Jul; Corry, P., Kozan, E., An assignment model for dynamic load planning of intermodal trains (2006) Comput. Oper. Res, 33 (1), pp. 1-17; Costantino, N., Dotoli, M., Falagario, M., Fanti, M.P., Mangini, A.M., A model for supply management of agile manufacturing supply chains (2012) Int. J. Prod. Econ, 135 (1), pp. 451-457; Dotoli, M., Fanti, M.P., Mangini, A.M., Stecco, G., Ukovich, W., The impact of ICT on intermodal transportation systems: A modelling approach by Petri nets (2010) Control Eng. Pract, 18 (8), pp. 893-903; Dotoli, M., Hammadi, S., Jeribi, K., Russo, C., Zgaya, H., A multiagent decision support system for optimization of co-modal transportation route planning services (2013) Proc. 52nd IEEE Conf. Decis. Control, Florence, Italy, pp. 911-916. , Dec; Dotoli, M., Epicoco, N., Falagario, M., Palma, D., Turchiano, B., A train load planning optimization model for intermodal freight transport terminals: A case study (2013) Proc. IEEE Conf. Syst. Man Cybern., Manchester, U. K., pp. 3597-3602. , Oct; Dotoli, M., Epicoco, N., Falagario, M., Cavone, G., A timed Petri nets model for intermodal freight transport terminals Proc. 12th IFAC, 12, pp. 176-181. , IEEE Int. Workshop Discret. Event Syst Cachan, France, May 2014; Dotoli, M., Epicoco, N., Falagario, M., Seatzu, C., Turchiano, B., Optimization of intermodal rail-road freight transport terminals (2014) Proc. IEEE Int. Conf. Robot. Autom, pp. 1971-1976. , Hong Kong, May; Dotoli, M., Epicoco, N., Falagario, M., Angelico, B., Vinciullo, A., A two-step optimization model for the pre-, and end-haulage of iners at intermodal freight terminals (2015) Proc. 14th IEEE Eur. Control Conf. (ECC), Linz, Austria, pp. 3477-3482. , Jul; Dotoli, M., Epicoco, N., Falagario, M., Costantino, N., Turchiano, B., An integrated approach for warehouse analysis, and optimization: A case study (2015) Comput. Ind, 70 (1), pp. 56-69; Dotoli, M., Epicoco, N., Falagario, M., Cavone, G., A timed Petri nets model for performance evaluation of intermodal freight transport terminals (2016) IEEE Trans. Autom. Sci. Eng, 13 (2), pp. 842-857. , Apr; Dyckhoff, H., A typology of cutting, and packing problems (1990) Eur. J. Oper. Res, 44 (2), pp. 145-159; Eng-Larsson, F., Kohn, C., Modal shift for greener logistics-The shipper's perspective (2012) Int. J. Phys. Distrib. Logist. Manag, 42 (1), pp. 36-59; Ferreira, L., Sigut, J., Modelling intermodal freight terminal operations (1995) Road Transport. Res, 4 (4), pp. 4-16; Gambardella, L.M., Rizzoli, A.E., Zaffalon, M., Simulation, and planning of an intermodal container terminal (1998) Simulation, 71 (2), pp. 107-116; Geng, G., Li, L.X., Scheduling railway freight cars (2001) Knowl. Based Syst, 14 (5-6), pp. 289-297; González, J.A., Ponce, E., Mataix, C., Carrasco, J., The automatic generation of transhipment plans for a train-Train terminal: Application to the Spanish-French border (2008) Transport. Plan. Technol, 31 (5), pp. 545-567; (2013) Annual Report 2013, , http://www.gtstrasporti.com/annual-report.html, GTS Online]. Available:; Günther, H.-O., Kim, K.H., Container terminals, and terminal operations (2006) OR Spectr, 28 (4), pp. 437-445; Harris, I., Wang, Y.L., Wang, H.Y., ICT in multimodal transport, and technological trends: Unleashing potential for the future (2015) Int. J. Prod. Econ, 159, pp. 88-103. , Jan; Imai, A., Sasaki, K., Nishimura, E., Papadimitriou, S., Multi-objective simultaneous stowage, and load planning for a container ship with container rehandle in yard stacks (2006) Eur. J. Oper. Res, 171 (2), pp. 373-389; Jaehn, F., Positioning of loading units in a transshipment yard storage area (2013) OR Spectr, 35 (2), pp. 399-416; Kengpol, A., Meethom, W., Tuominen, M., The development of a decision support system in multimodal transportation routing within Greater Mekong sub-region countries (2012) Int. J. Prod. Econ, 140 (2), pp. 691-701; Kim, K.H., Evaluation of the number of rehandles in container yards (1997) Comput. Ind. Eng, 32 (4), pp. 701-711; Kim, K.H., Lee, K.M., Hwang, H., Sequencing delivery, and receiving operations for yard cranes in port container terminals (2003) Int. J. Prod. Econ, 84 (3), pp. 283-292; Kozan, E., Preston, P., An approach to determine storage locations of containers at seaport terminals (2001) Comput. Oper. Res, 28 (10), pp. 983-995; Lee, D.-H., Wang, H.Q., Miao, L., Quay crane scheduling with non-interference constraints in port container terminals (2008) Transp. Res. e Logist. Transport. Rev, 44 (1), pp. 124-135; Legato, P., Mazza, R.M., Berth planning, and resources optimisation at a container terminal via discrete event simulation (2001) Eur. J. Oper. Res, 133 (3), pp. 537-547; Legato, P., Monaco, M.F., Human resources management at a marine container terminal (2004) Eur. J. Oper. Res, 156 (3), pp. 769-781; Martinez, F.M., Gutiérrez, I.G., Oliveira, A.O., Bedia, L.M.A., Gantry crane operations to transfer containers between trains: A simulation study of a Spanish terminal (2004) Transport. Plan. Technol, 27 (4), pp. 261-284; Murty, K.G., Liu, J., Wan, Y.-W., Linn, R., A decision support system for operations in a container terminal (2005) Decis. Support Syst, 39 (3), pp. 309-332; Murty, K.G., Hong Kong international terminals gains elastic capacity using a data-intensive decision-support system (2005) Interfaces, 35 (1), pp. 61-75; Shabayek, A.A., Yeung, W.W., A simulation model for the Kwai Chung container terminals in Hong Kong (2002) Eur. J. Oper. Res, 140 (1), pp. 1-11; Stahlbock, R., Voss, S., Operations research at container terminals: A literature update (2008) OR Spectr, 30 (1), pp. 1-52; Steenken, D., Voss, S., Stahlbock, R., Container terminal operation, and operations research-A classification, and literature review (2004) OR Spectr, 26 (1), pp. 3-49; (2006) Transport Res. Innov. Portal (TRIP), Brussels, Belgium, , Intermodal Freight Terminals-in Search of Efficiency to Support Intermodality Growth; (2011) UN Terminology on Combined Transport Prepared by the Economic Commission for Europe (UN/ECE), the European Conference of Ministers of Transport (ECMT), and the European Commission (EC), New York, and Geneva, , http://www.unece.org/fileadmin/DAM/trans/wp24/documents/term.pdf, United Nations Publications Catalogue. [Online]. Available:; Vis, I.F.A., De Koster, R., Transshipment of containers at a container terminal: An overview (2003) Eur. J. Oper. Res, 147 (1), pp. 1-16; Zajac, M., Restel, F.J., Bottlenecks of inland container terminals presented at the Probabil (2014) Safety Assess. Manag. Conf, , Hawaii, HI, USA, Jun; Zhang, C., Wan, Y.-W., Liu, J., Linn, R.J., Dynamic crane deployment in container storage yards (2002) Transport. Res. B Methodol, 36 (6), pp. 537-555; Zhang, C., Liu, J., Wan, Y.W., Murty, K.G., Linn, R.J., Storage space allocation in container terminals (2003) Transport. Res. B Methodol, 37 (10), pp. 883-903; Zhen, L., Lee, L.H., Chew, E.P., Chang, D.-F., Xu, Z.-X., A comparative study on two types of automated container terminal systems (2012) IEEE Trans. Autom. Sci. Eng, 9 (1), pp. 56-69. , Jan}, document_type={Article}, source={Scopus}, }`

- Carli, R., Dotoli, M., Garramone, R., Andria, G. & Lanzolla, A. M. L. (2017) An average consensus approach for the optimal allocation of a shared renewable energy source IN 2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 – Conference Proceedings., 270-275.

[Bibtex]`@CONFERENCE{Carli2017270, author={Carli, R. and Dotoli, M. and Garramone, R. and Andria, G. and Lanzolla, A.M.L.}, title={An average consensus approach for the optimal allocation of a shared renewable energy source}, journal={2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings}, year={2017}, pages={270-275}, doi={10.1109/SMC.2016.7844253}, art_number={7844253}, note={cited By 1}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85015803915&doi=10.1109%2fSMC.2016.7844253&partnerID=40&md5=8769614afb1c81c7bc718b4190b95117}, abstract={This paper investigates the problem of optimally distributing the energy produced by a shared renewable energy source among users, without relying on a centralized decision maker. We assume that each user is only allowed to communicate with his neighbors and buys energy from a producer under non-linear pricing. We formulate a quadratic programming problem aimed at ensuring a social welfare-optimal allocation of the shared resource. We propose a low-complexity distributed algorithm that relies on average consensus. We show the convergence of the proposed algorithm to the unique optimal solution of the resource allocation problem. We also provide numerical simulations demonstrating that the approach allows exploiting the potential of renewable energy sources' sharing to reduce users' energy consumption costs. © 2016 IEEE.}, author_keywords={Average consensus; Distributed optimization; Energy management; Multi-period resource allocation; Renewable energy sources}, keywords={Computational complexity; Cybernetics; Decision making; Economics; Energy management; Energy utilization; Natural resources; Optimization; Quadratic programming; Resource allocation, Average consensus; Distributed optimization; Multi-period; Non-linear pricing; Optimal allocation; Quadratic programming problems; Renewable energy source; Resource allocation problem, Renewable energy resources}, references={Adamo, F., Attivissimo, F., Di Nisio, A., Spadavecchia, M., Analysis of the uncertainty of the double-diode model of a photovoltaic panel (2011) Proc. IEEE Int. Instrumentation and Measurement Technology Conf, pp. 616-620. , Binjiang, China, May 10-12; Adamo, F., Attivissimo, F., Spadavecchia, M., A tool for Photovoltaic panels modeling and testing (2010) Proc. IEEE Int. Instrumentation and Measurement Technology Conf, pp. 1463-1466. , Austin, 3-6 May; Adamo, F., Cavone, G., Di Nisio, A., Lanzolla, A., Spadavecchia, M., A proposal for an open source energy meter (2013) Proc. IEEE Int. Instrumentation and Measurement Technology Conf, pp. 488-492. , Minneapolis, May 6-9; Attivissimo, F., Di Nisio, A., Lanzolla, A.M.L., Paul, M., Feasibility of a photovoltaic-thermoelectric generator: Performance analysis and simulation results (2015) IEEE Transactions on Instrumentation and Measurement, 64 (5), pp. 1158-1169. , May; Bertsekas, D.P., (1999) Nonlinear Programming, p. 780. , Belmont: Athena scientific; Boyd, S., Vandenberghe, L., (2004) Convex Optimization, , Cambridge University Press, UK; Carli, R., Dotoli, M., Energy scheduling of a smart home under nonlinear pricing (2014) Proc. IEEE Int. Conf. Dec. Contr., pp. 5648-5653. , Dec. 15-17; Carli, R., Dotoli, M., A decentralized resource allocation approach for sharing renewable energy among interconnected smart homes (2015) IEEE Int. Conf. Dec. Contr, , Dec. 15-18; Carli, R., Dotoli, M., Pellegrino R, R., A hierarchical decision making strategy for the energy management of smart cities (2016) IEEE Transactions on Automation Science and Engineering, , to appear; Cavraro, G., Carli, R., Zampieri, S., A distributed control algorithm for the minimization of the power generation cost in smart micro-grid (2014) Proc. IEEE Int. Conf. Dec. Contr., pp. 5642-5647. , Dec. 15-17; Franceschelli, M., Giua, A., Seatzu, C., Distributed averaging in sensor networks based on broadcast gossip algorithms (2011) IEEE Sensors Journal, 3 (3), pp. 808-817; Garin, F., Schenato, L., Networked control systems Springer, 2011, Ch. A Survey on Distributed Estimation and Control Applications Using Linear Consensus Algorithms, pp. 75-107; Guérin, F., Lefebvre, D., Mboup, A.B., Parédé, J., Lemains, E., Ndiaye, P.A.S., Hybrid modeling for performance evaluation of multisource renewable energy systems (2011) IEEE Trans. Aut. Sci. Eng., 8 (3), pp. 570-580. , July; Huang, Z., Zhu, T., Gu, Y., Irwin, D., Mishra, A., Shenoy, P., Minimizing electricity costs by sharing energy in sustainable microgrids (2014) Proc. ACM Conf. Embed. Sys. Ener.-Effic. Build., pp. 120-129. , Nov; Katoh, N., Shioura, A., Ibaraki T, T., Resource allocation problems (2013) Handbook of Combinatorial Optimization, pp. 2897-2988; Loia, V., Terzija, V., Vaccaro, A., Wall, P., An affine-arithmetic-based consensus protocol for smart-grid computing in the presence of data uncertainties (2015) IEEE Transactions on Industrial Electronics, 62 (5), pp. 2973-2982. , May; Loia, V., Vaccaro, A., Decentralized economic dispatch in smart grids by self-organizing dynamic agents (2014) IEEE Transactions on Systems, Man, and Cybernetics: Systems, 44 (4), pp. 397-408. , April; Rodriguez-Amenedo, J.L., Arnalte, S., Burgos, J.C., Automatic generation control of a wind farm with variable speed wind turbines (2002) IEEE Trans. Energy Conversion, 17 (2), pp. 279-284. , Jun; Vaccaro, A., Loia, V., Formato, G., Wall, P., Terzija, V., A self-organizing architecture for decentralized smart microgrids synchronization, control, and monitoring (2015) IEEE Transactions on Industrial Informatics, 11 (1), pp. 289-298. , Feb; Wu, Y., Lau, V.K.N., Tsang, D.H.K., Qian, L.P., Meng, L., Optimal energy scheduling for residential smart grid with centralized renewable energy source (2014) Systems Journal, IEEE, 8 (2), pp. 562-576. , June; Xiao, L., Boyd, S., Fast linear iterations for distributed averaging (2004) Systems and Control Letters, 53 (1), pp. 65-78. , September; Zhong, W., Huang, Z., Zhu, T., Gu, Y., Zhang, Q., Yi, P., Jiang, D., Xiao, S., IDES: Incentive-driven distributed energy sharing in sustainable microgrids (2014) Green Computing Conference (IGCC) 2014, pp. 1-10. , 3-5 Nov; Zhu, T., Huang, Z., Sharma, A., Su, J., Irwin, D., Mishra, A., Shenoy, P., Sharing renewable energy in smart microgrids (2013) Proc. ACM/IEEE Int. Conf. Cyber-Phys. Sys., pp. 219-228. , April}, document_type={Conference Paper}, source={Scopus}, }`

- Dotoli, M. & Epicoco, N. (2017) A technique for the optimal management of containers’ drayage at intermodal terminals IN 2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 – Conference Proceedings., 566-571.

[Bibtex]`@CONFERENCE{Dotoli2017566, author={Dotoli, M. and Epicoco, N.}, title={A technique for the optimal management of containers' drayage at intermodal terminals}, journal={2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings}, year={2017}, pages={566-571}, doi={10.1109/SMC.2016.7844300}, art_number={7844300}, note={cited By 3}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85015742748&doi=10.1109%2fSMC.2016.7844300&partnerID=40&md5=b4e0e9ba544f88a650a674ec969427ea}, abstract={This paper focuses on optimizing one of the most critical activities in door-to-door intermodal transportation, i.e., the containers' drayage by road. We present a technique to solve in an exact and optimal way the pick-up and delivery problem under the typical assumptions of intermodal transportation: full truck load, split delivery, clustered backhauls, and time windows. The method allows limiting the distance traveled by road, enabling to match a delivery with a pick-up request, while respecting customers' service time windows, vehicles availability, and rental needs. Thus, intermodal companies can manage vehicle routing and scheduling problems in an integrated way. The technique effectiveness is shown by a real case study. © 2016 IEEE.}, keywords={Containers; Cybernetics; Pickups; Roads and streets; Truck transportation; Vehicle routing, Critical activities; Full truck loads; Intermodal terminals; Optimal management; Pickup and delivery; Split delivery; Time windows; Vehicle routing and scheduling, Intermodal transportation}, references={Dotoli, M., Epicoco, N., Falagario, M., Seatzu, C., Turchiano, B., A decision support system for optimizing operations at intermodal rail-road terminals IEEE Trans Sys Man Cyb: Sys, 15. , (in press); Cavone, G., Dotoli, M., Seatzu, C., Management of intermodal freight terminals by first-order Hybrid Petri Nets (2016) IEEE Rob Aut Let, 1 (1), pp. 2-9; Dotoli, M., Epicoco, N., Falagario, M., Cavone, G., A Timed Petri Nets model for performance evaluation of intermodal freight transport terminals (2016) IEEE Trans Aut Sci Eng, 13 (2), pp. 842-857; Dotoli, M., Epicoco, N., Falagario, M., A technique for the efficient multimodal transport planning under multiple conflicting objectives and uncertainty (2016) 15th European Control Conf, , Aalborg, June 29-July 1; Macharis, C., Bontekoning, Y.M., Opportunities for or in intermodal freight transport research: A review (2004) Eur J Oper Res, 153 (2), pp. 400-416; Dotoli, M., Epicoco, N., Falagario, M., Palma, D., Turchiano, B., A train load planning optimization model for intermodal freight transport terminals: A case study (2013) Proc IEEE Int Conf Sys Man Cyber, pp. 3597-3602; Dotoli, M., Epicoco, N., Falagario, M., Seatzu, C., Turchiano, B., Optimization of intermodal rail-road freight transport terminals (2014) Proc IEEE Int Conf Robot Autom, pp. 1971-1976; Harris, I., Wang, Y.L., Wang, H.Y., ICT in multimodal transport and technological trends: Unleashing potential for the future (2015) Int J Prod Econ, 159, pp. 88-103; Caris, A., Janssens, G., A local search heuristic for the pre-and end-haulage of intermodal container terminals (2009) Comput Oper Res, 36 (10), pp. 2763-2772; Vidovi, M., Nikoli, M., Popovi, D., Two mathematical formulations for the containers drayage problem with time windows (2012) Int J Business Science and Applied Management, 7 (3), pp. 23-32; Lai, M., Di Francesco, M., Zuddas, P., Heuristic for the routing of trucks with double container loads (2012) Proc 3rd Student Conf on Operational Research, Nottingham, pp. 84-93; Dotoli, M., Epicoco, N., Falagario, M., Angelico, B., Vinciullo, A., A two-step optimization model for the pre-and end-haulage of containers at intermodal freight terminals (2015) Proc 14th European Control Conf, pp. 3477-3482; Eksioglu, B., Vural, A.V., Reisman, A., The vehicle routing problem: A taxonomic review (2009) Comput Ind Eng, 57, pp. 1472-1483; Parragh, S.N., Doerner, K.F., Hartl, R.F., A survey on pickup and delivery problems (2008) J Betriebswirtschaft, 58, pp. 81-117; Toth, P., Vigo, D., The vehicle routing problem (2001) SIAM Monographs on Discrete Mathematics and Applications, , Philadelphia, USA; Laporte, G., What you should know about the vehicle routing problem (2007) Nav Res Log, 54 (8), pp. 811-819; Jemai, J., Zekri, M., Mellouli, K., An NSGA-II algorithm for the green Vehicle Routing Problem (2012) LNCS, 7245, pp. 37-48. , J.-K. Hao and M. Middendorf (Eds); Cordeau, J.F., Desaulniers, G., Desrosiers, J., Solomon, M.M., Soumis, F., VRP with time windows (2002) The Vehicle Routing Problem, 9, pp. 175-193. , P. Toth and D. Vigo (Eds); Drexl, M., Applications of the Vehicle Routing Problem with trailers and transshipments (2013) Eur J Oper Res, 227 (2), pp. 275-284; Janssens, G.K., Braekers, K., An exact algorithm for the Full Truckload Pick-up and Delivery Problem with Time Windows: Concept and implementation details (2011) Proc 25th European Simulation and Modelling Conf, pp. 257-262; Li, J., Lu, W., Full truckload vehicle routing problem with profits (2014) J Traffic Transportation Eng, 1, pp. 146-152; Chung, K.H., Ko, C.S., Shin, J.Y., Hwang, H., Kim, K.H., Development of mathematical models for the container road transportation in Korean trucking industries (2007) Comput Ind Eng, 53, pp. 252-262; Tan, K.C., Chew, Y.H., Lee, L.H., A hybrid multi-objective evolutionary algorithm for solving truck and trailer Vehicle Routing Problem (2006) Eur J Oper Res, 172, pp. 855-885; Reinhardt, L.B., Spoorendonk, S., Pisinger, D., Solving vehicle routing with full container load and time windows (2012) 3rd Int Conf Computational Logistics, pp. 120-128; Wang, X., Regan, A.C., Local truckload pickup and delivery with hard time window constraints (2002) Transport Res B Meth, 36 (2), pp. 97-112; Lai, M., Battarra, M., Di Francesco, M., Zuddas, P., An adaptive guidance meta-heuristic for the Vehicle Routing Problem with Splits and Clustered Backhauls (2014) J Oper Res Soc, 66; Imai, A., Nishimura, E., Current, J., A lagrangian relaxation-based heuristic for the vehicle routing with full container load (2007) Eur J Oper Res, 176 (1), pp. 87-105; Schönberger, J., Buer, T., Kopfer, H., A Model for the coordination of 20-foot and 40-foot container movements in the hinterland of a container terminal (2013) LNCS, 8197, pp. 113-127; Vidovi, M., Radivojevic B, G., Rakovic, Vehicle routing in containers Pickup up and Delivery processes (2011) Procedia, 20, pp. 335-343; Levinson, M., (2016) The Box: How the Shipping Container Made the World Smaller and the World Economy Bigger, , Princeton University Press, 2nd ed}, document_type={Conference Paper}, source={Scopus}, }`

- Ben Othman, S., Zgaya, H., Dotoli, M. & Hammadi, S. (2017) An agent-based Decision Support System for resources’ scheduling in Emergency Supply Chains. IN Control Engineering Practice, 59.27-43.

[Bibtex]`@ARTICLE{BenOthman201727, author={Ben Othman, S. and Zgaya, H. and Dotoli, M. and Hammadi, S.}, title={An agent-based Decision Support System for resources' scheduling in Emergency Supply Chains}, journal={Control Engineering Practice}, year={2017}, volume={59}, pages={27-43}, doi={10.1016/j.conengprac.2016.11.014}, note={cited By 28}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84999780416&doi=10.1016%2fj.conengprac.2016.11.014&partnerID=40&md5=80bf4e8f6db808a74e12a1d923d84124}, abstract={We propose a multi-agent-based architecture for the management of Emergency Supply Chains (ESCs), in which each zone is controlled by an agent. A Decision Support System (DSS) states and solves, in a distributed way, the scheduling problem for the delivery of resources from the ESC supplying zones to the ESC crisis-affected areas. Thanks to the agents’ cooperation, the DSS provides a scheduling plan that guarantees an effective response to emergencies. The approach is applied to two real cases: the Mali and the Japan crisis. Simulations are based on real data that have been validated by a team of logisticians from Airbus Defense and Space. © 2016 Elsevier Ltd}, author_keywords={Crisis management; Decision Support System; Emergency Supply Chain; Multi-agent system; Scheduling}, keywords={Artificial intelligence; Multi agent systems; Scheduling; Supply chains, Affected area; Agent-based decision support systems; Crisis management; Decision support system (dss); Multi agent; Real case; Scheduling problem, Decision support systems}, references={Adam, E., Model of multi-agent organization to aid cooperative work in business processes: application to complex business systems (2000), (Ph.D. Thesis) University of Valenciennes and Hainaut-Cambresis Valenciennes, France; ALP Advanced Logistics Project Homepage, Defense advanced research projects agency, , http://www.darpa.mil/iso2/alp; Altay, N., Green, W., OR/MS research in disaster operations management (2006) European Journal of Operational Research, 175 (1), pp. 475-493; Asghar, S., Alahakoon, D., Churilov, L., Dynamic integrated model for decision support systems (2005) International Journal of Simulation Modelling, 6 (10), p. 11; Barahona, F., (2013). Agile logistics simulation and optimization for managing disaster responses. In Proceedings of the winter simulation conference, Washington D.C; Barbati, M., Bruno, G.G., Genovese, A., Applications of agent-based models for optimization problems: A literature review (2012) Expert Systems With Applications, 39, pp. 6020-6028; Ben-Tal, A., Do Chung, B., Mandala, S., Yao, T., Robust optimization for emergency logistics planning: Risk mitigation in humanitarian relief supply chains (2011) Transportation Research: Part B, 45, pp. 1177-1189; Bruno, W., Célia, G., Towards a cognitive meta-model for adaptive trust and reputation in open multi-agent systems (2015) Autonomous Agents and Multi-Agent Systems Archive, 29 (6), pp. 1125-1156; Cammarata, S., McArthur, D., Steeb, R., Strategies of cooperation in distributed problem solving (1988) Readings in distributed artificial intelligence, pp. 102-105. , Alan H. Bond Les Gasser Morgan Kaufmann Santa Monica, CA; Chaib-draa, B., Industrial applications of distributed AI (1995) Communication of ACM, 38 (11), pp. 49-53; Costantino, N., Dotoli, M., Falagario, M., Fanti, M.P., Mangini, A.M., A model for supply management of agile manufacturing supply chains (2012) International Journal of Production Economics, 135 (1), pp. 451-457; Dotoli, M., Epicoco, N., Falagario, M., (2016) A fuzzy technique for supply chain network design with quantity discounts, International Journal of Production Research, special issue on “IFAC MIM-INCOM Conferences, pp. 1-23. , http://dx.doi.org/10.1080/00207543.2016.11784008; Duflos, E., Vanheeghe, P., (2000). Estimation Prédiction, CollectionSciences et Technologies dirigée par P. Borne. Edition Technip (in French), Paris, France; www.fipa.org; Flichy, T., (2013). Opération Serval au Mali. L'intervention française décryptée. Paris, Lavauzelle (in French); Gaonkar, R.S., Viswanadham, N., Analytical framework for the management of risk in supply chains (2007) IEEE Transactions on Automation Science and Engineering, 4 (2). , (265,273); Garcìa-Magarino, I., Gutiérrez, C., Fuentes-Fernàndez, R., (2009). The INGENIAS Development Kit: a practical application for crisis-management. In Proceedings of the 10th international conference artif. neur. netw. (IWANN); Greenwood, D., (2005). JADE Web Service Integration Gateway (WSIG). Whitestein Technologies, Jade Tutorial, AAMAS; Guojun, J., Caihong, Z., A study on emergency supply chain and risk based on urgent relief service in disasters (2012) Systems Engineering Procedia, 5, pp. 313-325; Gupta, M., Ko, H.J., Min, H., TOC-based performance measures and five focusing steps in jobshop manufacturing environment (2002) International Journal of Production Research, 40 (4), pp. 907-930; Hale, T., Moberg, C.R., Improving supply chain disaster preparedness, A decision process for secure site location (2005) International Journal of Physical Distribution & Logistics Management, 35 (3), pp. 195-207; Kaddoussi, A., Zgaya, H., Hammadi, S., Bretaudeau, F., PAAN: Partial Agreement Negotiation Network based on intelligent agents in crisis situation (2009) International Journal of Mathematics and Computers in Simulation, 3 (4); Kaddoussi, A., Zgaya, H., Hammadi, S., Duflos, E., P.Vanheeghe (2012). Need estimating agent-based tool for resources forecasting. In Proceedings of the 16th world multi-conference systemics, cybernetics and informatics: WMSCI 2012. July 17th–20th. Orlando, Florida, USA; Kaddoussi, A., Zoghlami, N., Hammadi, S., Zgaya, H., An agent-based distributed scheduling for crisis management supply chain (2013) International Journal of Intelligent Systems, 6 (1), pp. 156-173; Kelle, P., Schneider, H., Yi, H., Decision alternatives between expected cost minimization and worst case scenario in emergency supply (2014) International Journal of Production Economics, 157, pp. 250-260; Lee, Y.H., Kim, S.H., Moon, C., Production distributed planning in supply chain using a hybrid approach (2002) Production Planning & Control, 13 (1), pp. 35-46; Luder, A., Peschke, J., Sauter, T., Deter, S., Diep, D., Distributed Intelligence for plant automation based on multi-agent systems: the PABADIS approach (2004) Production Planning & Control, 15 (2), pp. 201-212; Mařík, V., Lažanský, J., Industrial applications of agent technologies (2007) Control Engineering Practice, 15 (11), pp. 1364-1380; Maturana, F., Shen, W., Norrie, D.H., Multi-agent mediator architecture for distributed manufacturing (1999) Journal of Intelligent Manufacturing, 7, pp. 257-270; Morel, G., Valckenaers, P., Faure, J.-M., Pereira, C.E., Diedrich, C., Manufacturing plant control challenges and issues (2007) Control Engineering Practice, 15 (11), pp. 1321-1331; Nagurney, A., Amir Masoumi, H. and Yu, M. (2014). An Integrated disaster relief supply chain network model with time targets and demand uncertainty (pp. 287–318). Springer International, Berlin; Nagurney, A., Yu, M., Qiang, Q., (2011). Supply chain network design for critical needs with outsourcing, Papers, vol. 90, no. 1. Region. Sci. (pp. 123–143); Nyugen, B.U., Reding, D.F., Nyugen, D., An engagement model to optimize defense against multiple attack assuming perfect kill assessment (1997) Naval Research Logistics, 44 (7), pp. 687-697; Othman, S.B., Zoghlami, N., Zgaya, H., Hammadi, S., Adaptive collaborative agent-based system for crisis management (2014) Proceedings IEEE/WIC/ACM International Conference Intell. Ag. Tech., pp. 11-14. , Warsaw (Poland; Picco, G.P., Baldi, M., (1998). Evaluating the tade-offs of mobile code design paradigms in network management applications. In Proceedings of the 20th IEEE nternational conference softw. eng. (ICSE’97), (pp. 146–155). Kyoto, Japan; Rao, A.S., Georgeff, M.P., (1995). BDI agents: from theory to practice. In Proceedings of the first international conference on multi-agent systems (ICMAS-95), San Francisco, CA; Sauer, J., Knowledge-based scheduling techniques in industry (1999), CRC Press Boca Raton, FL; Saint Germain, B., Valckenaers, P., Verstraete, P., Hadeli, Brussel, H.V., A multi-agent supply network control framework (2007) Control Engineering Practice, 15 (11), pp. 1394-1402; Shen, W., Hao, Q., Yoon, H., Norrie, D.H., Applications of agent-based systems in intelligent manufacturing: An updated review (2006) Advanced Engineering Materials, 20 (4). , (415‐431); Sheu, J., An emergency logistics distribution approach for quick response to urgent relief demand in disasters (2007) Transportation Research Part E, 43, pp. 687-709; Sheu, J.B., Dynamic relief-demand management for emergency logistics operations under large-scale disasters (2010) Transportation Research Part E, 46, pp. 1-17; Sheu, J., Pan, C., A method for designing centralized emergency supply network to respond to large-scale natural disasters (2014) Transportation Research: Part B, 67, pp. 284-305; Tang, C.S., Perspective in supply risk management. Review (2006) International Journal of Production Economics, 103 (1), pp. 451-488; Yongsong, Z., Siuming, L., Kwokkit, Y., Research of crisis management system based on CG and GIS Proceedings IEEE international conference comp. sci. aut. eng., CSAE, pp. 596-599. , Shanghai, China; Wang, J., Emergency response workflow resource requirements modeling and analysis (2009) IEEE Transactions on Systems, Man, And Cybernetics—Part C: Applications and Reviews, 39 (3); Wang, N., Chen Y. and Zhang L. (2011). Design of multi-agent based distributed scheduling system for bus rapid Transit. In Proceedings of the 3rd int. conf. international conference intell. hum.-mach. sys cyb. (pp. 111–114), Hangzhou, China; Woolridge, M., Jenning, N., Intelligent agents: Theory and practice (1995) Knowledge Engineering Review, 2 (10), pp. 115-152; Zoghlami, N., Hammadi, S., (2006). Estimator agent approach for distributed logistic chain optimization. In Proc. IEEE int. conf. ANIPLA’ (pp. 13–15). Rome, Italy}, document_type={Article}, source={Scopus}, }`

- Dotoli, M., Epicoco, N. & Falagario, M. (2017) A technique for efficient multimodal transport planning with conflicting objectives under uncertainty IN 2016 European Control Conference, ECC 2016., 2441-2446.

[Bibtex]`@CONFERENCE{Dotoli20172441, author={Dotoli, M. and Epicoco, N. and Falagario, M.}, title={A technique for efficient multimodal transport planning with conflicting objectives under uncertainty}, journal={2016 European Control Conference, ECC 2016}, year={2017}, pages={2441-2446}, doi={10.1109/ECC.2016.7810656}, art_number={7810656}, note={cited By 4}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85015015532&doi=10.1109%2fECC.2016.7810656&partnerID=40&md5=64b0c90acc204f7f0a66eee29673487d}, abstract={Multimodal freight transport is growing as a means to reduce environmental impact and road congestion, and to increase road safety. The proper planning and management of multimodal transport is a key issue to ensure the competitiveness of companies. In this paper we present a fuzzy cross-efficiency Data Envelopment Analysis (DEA) technique for efficient multimodal transport planning in a multi-objective perspective, under uncertainty, and with a high discriminative power. The approach is tested on a real case study, showing its effectiveness in determining the most efficient transport planning and in identifying the distance from which multimodality is more efficient than all-road transport. © 2016 EUCA.}, keywords={Data envelopment analysis; Environmental impact; Freight transportation; Motor transportation; Roads and streets; Transportation; Uncertainty analysis, Conflicting objectives; Cross efficiency; Data envelopment analysis technique; Discriminative power; Freight transport; Multimodal transport; Road transports; Transport planning, Multimodal transportation}, references={Analysis of the contribution of transport policies to the competitiveness of the EU economy and comparison with the United States (2006) Compete Final Report, , EC, European Commission; Dotoli, M., Epicoco, N., Falagario, M., Seatzu, C., Turchiano, B., Optimization of intermodal rail-road freight transport terminals Proc. 2014 IEEE Int Conf Robot Autom, pp. 1971-1976. , Hong Kong; Dotoli, M., Epicoco, N., Falagario, M., Seatzu, C., Turchiano, B., A Decision Support System for optimizing operations at intermodal railroad terminals (2016) IEEE Trans Syst Man Cybern, , in press; Dotoli, M., Epicoco, N., Falagario, M., Cavone, G., A timed Petri Nets model for performance evaluation of intermodal freight transport terminals (2015) IEEE Trans Autom Sci Eng, , in press; Harris, I., Wang, Y.L., Wang, H.Y., ICT in multimodal transport and technological trends: Unleashing potential for the future Int J Prod Econ, 159 (2015), pp. 88-103; Mathisen, T.A., Hanssen, T.E.S., Jørgensen, F., Larsen, B., Ranking of transport modes - Intersections between price curves for transport by truck, rail, and water European Transport, 57 (2015), pp. 1-14; Rodrigue, J.P., Comtois, C., Slack, B., (2013) The Geography of Transportation System, p. 416. , 3rd Ed., Routledge; Meixwell, M.J., Norbis, M., A review of the transportation mode choice and carrier selection literature (2008) Int J Logis Manag, 19 (2), pp. 183-211; Min, H., International intermodal choices via change-constrained goal programming (1991) Transp Res, 25 (6), pp. 351-362; Chang, T., Best routes selection in international intermodal networks (2008) Comput Oper Res, 35 (9), pp. 2877-2891; Verma, M., Verter, V., Zufferey, N., A bi-objective model for planning and managing rail-truck intermodal transportation of hazardous materials (2012) Eur Res, 48 (1), pp. 132-149; Bierwirth, C., Kirschstein, T., Meisel, F., On transport service selection in intermodal rail/road distribution networks (2012) Business Research Official Open Access Journal of VHB, 5 (2), pp. 198-219; Bray, S., Caggiani, L., Dell'Orco, M., Ottomanelli, M., Measuring transport systems efficiency under uncertainty by fuzzy sets theory based Data Envelopment Analysis (2014) Procedia, 111, pp. 770-779; Hanaoka, S., Kunadhamraks, P., Multiple criteria and fuzzy based evaluation of logistics performance for intermodal transportation (2009) J Adv Transport, 43 (2), pp. 123-153; Dotoli, M., Epicoco, N., Falagario, M., Palma, D., Turchiano, B., A train load planning optimization model for intermodal freight transport terminals: A case study 2013 IEEE Int Conf Systems Man Cybernetics, pp. 3597-3602; Gursoy, M., A decision supportive method for multimodal freight transport mode choice: An example from Turkey (2010) Iranian J Science & Technology, 34 (4), pp. 461-470; Kengpol, A., Meethom, W., Tuominen, M., The development of a Decision Support System in multimodal transportation routing within Greater Mekong sub-region countries (2012) Int J Prod Econ, 140 (2), pp. 691-701; Kopytov, E., Abramov, D., Multiple-criteria analysis and choice of transportation alternatives in multimodal freight transport system (2012) Transport and Telecommunication J, 13 (2), pp. 148-158; Qu, L., Chen, Y., Mu, X., A transport mode selection method for multimodal transportation based on an adaptive ANN system (2008) 4th Int Conf Natural Computation, 3, pp. 436-440; Charnes, A., Cooper, W., Rodes, E., Measuring the efficiency of decision making (1978) Eur J Oper Res, 2, pp. 429-444; Doyle, J., Green, R., Efficiency and cross-efficiency in DEA: Derivation, meanings and uses (1994) J Oper Res Soc, 45, pp. 567-578; Dotoli, M., Epicoco, N., Falagario, M., Sciancalepore, F., A crossefficiency fuzzy data envelopment analysis technique for performance evaluation of decision making units under uncertainty Comput Ind Eng, 79 (2015), pp. 103-114; Jimenez, M., Bilbao, A., Pareto-optimal solution in fuzzy multiobjective linear programming (2009) Fuzzy Set Syst, 160, pp. 2714-2721; http://www.gtstrasporti.com; Pastori, E., Tagliavia, M., Tosti, E., Zappa, S., L'indagine sui costi del trasporto internazionale delle merci in Italia: Metodi e risultati (2014) Questioni di Economia e Finanza, in Italian; Ecotransit, , http://www.ecotransit.org; Maibach, M., Schreyer, C., Sutter, D., Handbook on estimation of external cost in the transport sector (2008) CE Delft; http://www.arpa.vda.it, in Italian; (2009) Rapporto Sull'incidentalità Nei Trasporti, , MIT, Ministero delle Infrastrutturee dei Trasporti, in Italian; Dotoli, M., Epicoco, N., Falagario, M., Sciancalepore, F., A stochastic cross-efficiency Data Envelopment Analysis approach for supplier selection under uncertainty (2016) Int T Oper Res, 23 (4), pp. 725-748}, document_type={Conference Paper}, source={Scopus}, }`

### 2016

- Carli, R., Dotoli, M., Andria, G. & Lanzolla, A. M. L. (2016) Bi-level programming for the strategic energy management of a smart city IN EESMS 2016 – 2016 IEEE Workshop on Environmental, Energy, and Structural Monitoring Systems, Proceedings..

[Bibtex]`@CONFERENCE{Carli2016, author={Carli, R. and Dotoli, M. and Andria, G. and Lanzolla, A.M.L.}, title={Bi-level programming for the strategic energy management of a smart city}, journal={EESMS 2016 - 2016 IEEE Workshop on Environmental, Energy, and Structural Monitoring Systems, Proceedings}, year={2016}, doi={10.1109/EESMS.2016.7504820}, art_number={7504820}, note={cited By 4}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84980371756&doi=10.1109%2fEESMS.2016.7504820&partnerID=40&md5=645711d93f3fee6a0e376fe0decdd093}, abstract={This paper addresses the emerging need for tools devoted to the strategic energy management of smart cities. We propose a novel decision model that supports the energy manager in governing the smart city while addressing different urban sectors with an integrated and structured energy retrofit planning. A bi-level programming model integrates several decision making units (decision panels), each focusing on the energy optimization of a specific urban subsystem, and a central decision unit. We solve the bi-level decision problem by a game theoretic distributed approach. We apply the developed decision model to the case study of the city of Bari (Italy), where a smart city program has recently been launched. © 2016 IEEE.}, author_keywords={bi-level programming; decision support system; distributed optimization; energy efficiency; energy management; smart city}, keywords={Artificial intelligence; Decision support systems; Distributed computer systems; Energy efficiency; Energy management; Game theory; Monitoring, Bi-level programming; Bilevel programming models; Decision making unit; Distributed approaches; Distributed optimization; Energy optimization; Smart cities; Strategic energy management, Decision making}, references={Albino, V., Berardi, U., Dangelico, R.M., Smart cities: Definitions, dimensions, performance, and initiatives (2014) Journal of Urban Technology, (21); Başar, T., Olsder, G.J., Dynamic noncooperative game theory (1999) SIAM Series in Classics in Applied Mathematics, , Philadelphia, PA:SIAM; Belotti, P., Kirches, C., Leyffer, S., Linderoth, J., Luedtke, J., Mahajan, A., Mixed-integer nonlinear optimization (2013) Acta Numerica, 22, pp. 1-131; Calvillo, C.F., Sánchez-Miralles, A., Villar, J., Energy management and planning in smart cities (2016) Renewable and Sustainable Energy Reviews, 55, pp. 273-287; Caponio, G., Massaro, V., Mossa, G., Mummolo, G., Strategic Energy Planning of Residential Buildings in a Smart City: A System Dynamics Approach (2015) International Journal of Engineering Business Management., 2015; Caragliu, A., Del Bo, C., Nijkamp, P., Smart cities in Europe (2009) Proc. 3rd Centr. Europ. Conf. Regional Science, , Oct; Carli, R., Deidda, P., Dotoli, M., Pellegrino, R., An urban control center for the energy governance of a smart city (2014) Proc. IEEE ETFA2014, pp. 1-7; Carli, R., Dotoli, M., Pellegrino, R., Ranieri, L., Measuring and managing the smartness of cities: A framework for classifying performance indicators (2013) Proc. IEEE SMC2013, pp. 1288-1293; Carli, R., Albino, V., Dotoli, M., Mummolo, G., Savino, M., A dashboard and decision support tool for the energy governance of smart cities (2015) Environmental, Energy and Structural Monitoring Systems (EESMS), 2015 IEEE Workshop on, pp. 23-28. , 9-10 July; Carli, R., Dotoli, M., Pellegrino, R., Ranieri, L., A decision making technique to optimize a building stock energy efficiency (2016) IEEE Trans. Sys., Man Cyb.: Systems; Gümüş, Z.H., Floudas, C.A., Global optimization of mixedinteger bilevel programming problems (2005) Computational Management Science, 2 (3), pp. 181-212; Kornai, J., Liptak, T., Two-level planning (1965) Econometrica: Journal of the Econometric Society, pp. 141-169; Lust, T., Teghem, J., The multiobjective multidimensional knapsack problem: A survey and a new approach (2012) International Transactions in Operational Research, 19 (4), pp. 495-520; Vicente, L.N., Calamai, P.H., Bilevel and multilevel programming: A bibliography review (1994) J. Glob. Optim., 5 (3), pp. 291-306; UN-HABITAT, (2011) Cities and Climate Change: Policy Directions, Global Report on Human Settlement}, document_type={Conference Paper}, source={Scopus}, }`

- Dotoli, M., Epicoco, N., Falagario, M. & Sciancalepore, F. (2016) A stochastic cross-efficiency data envelopment analysis approach for supplier selection under uncertainty. IN International Transactions in Operational Research, 23.725-748.

[Bibtex]`@ARTICLE{Dotoli2016725, author={Dotoli, M. and Epicoco, N. and Falagario, M. and Sciancalepore, F.}, title={A stochastic cross-efficiency data envelopment analysis approach for supplier selection under uncertainty}, journal={International Transactions in Operational Research}, year={2016}, volume={23}, number={4}, pages={725-748}, doi={10.1111/itor.12155}, note={cited By 46}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84959365566&doi=10.1111%2fitor.12155&partnerID=40&md5=05c4875bc723279001f5267f45504640}, abstract={This paper addresses one of the key objectives of the supply chain strategic design phase, that is, the optimal selection of suppliers. A methodology for supplier selection under uncertainty is proposed, integrating the cross-efficiency data envelopment analysis (DEA) and Monte Carlo approach. The combination of these two techniques allows overcoming the deterministic feature of the classical cross-efficiency DEA approach. Moreover, we define an indicator of the robustness of the determined supplier ranking. The technique is able to manage the supplier selection problem considering nondeterministic input and output data. It allows the evaluation of suppliers under uncertainty, a particularly significant circumstance for the assessment of potential suppliers. The novel approach helps buyers in choosing the right partners under uncertainty and ranking suppliers upon a multiple sourcing strategy, even when considering complex evaluations with a high number of suppliers and many input and output criteria. © 2016 The Authors.}, author_keywords={Data envelopment analysis; Monte Carlo method; Supplier evaluation; Uncertainty modeling}, keywords={Data envelopment analysis; Efficiency; Monte Carlo methods; Stochastic systems; Uncertainty analysis, Complex evaluations; Input and outputs; Monte Carlo approach; Optimal selection; Sourcing strategies; Supplier Evaluations; Supplier selection; Uncertainty modeling, Supply chains}, references={Adler, N., Friedman, L., Sinuany-Stern, Z., Review of ranking methods in the data envelopment analysis context (2002) European Journal of Operational Research, 140, pp. 249-265; Albino, V., Garavelli, A.C., A neural network application to subcontractor rating in construction firms (1998) International Journal of Project Management, 16 (1), pp. 9-14; Andersen, P., Petersen, N.C., A procedure for ranking efficient units in data envelopment analysis (1993) Management Science, 39 (10), pp. 1261-1264; Araz, C., Ozkarahan, I., Supplier evaluation and management system for strategic sourcing based on a new multicriteria sorting procedure (2007) International Journal of Production Economics, 106, pp. 585-606; Bhattacharya, A., Mohapatra, P., Kumar, V., Dey, P.K., Brady, M., Tiwari, M.K., Nudurupati, S.S., Green supply chain performance measurement using fuzzy ANP-based balanced scorecard: a collaborative decision-making approach (2013) Production Planning & Control, 25 (8), pp. 698-714; Burke, G.J., Vakharia, A.J., Supply chain management (2004) The Internet Encyclopedia, , In Bidgoli, H. (ed.), John Wiley & Sons, New York; Cazals, C., Florens, J.P., Simar, L., Nonparametric frontier estimation: a robust approach (2002) Journal of Econometrics, 106 (1), pp. 1-25; Charnes, A., Cooper, W.W., Rhodes, E., Measuring the efficiency of decision making units (1978) European Journal of Operational Research, 2, pp. 429-444; Che, Z.H., Wang, H.S., A hybrid approach for supplier cluster analysis (2010) Computers and Mathematics with Applications, 59, pp. 745-763; Cooper, W.W., Huang, Z., Lee, S.X., Satisficing DEA models under chance constraints (1996) Annals of Operations Research, 66, pp. 279-295; Cooper, W.W., Seiford, L.M., Tone, K., (2007) Data Envelopment Analysis: A Comprehensive Text with Models, Applications, References and DEA-Solver Software, , 2nd edn. Springer, New York; Costantino, N., Dotoli, M., Epicoco, N., Falagario, M., Sciancalepore, F., A Cross Efficiency Fuzzy Data Envelopment Analysis Technique for Supplier Evaluation Under Uncertainty (2012) ETFA Conference 2012, , September 17-21, Krakòw, Poland; Costantino, N., Dotoli, M., Epicoco, N., Falagario, M., Sciancalepore, F., Using Cross-Efficiency Fuzzy Data Envelopment Analysis for Healthcare Facilities Performance Evaluation Under Uncertainty (2013) Proc. 2013 IEEE International Conference on Systems, Man, and Cybernetics SMC 2013, pp. 912-917. , October 13-16, Manchester, UK; Costantino, N., Dotoli, M., Falagario, M., Fanti, M.P., Iacobellis, G., A decision support system framework for purchasing management in supply chains (2009) Journal of Business and Industrial Marketing, 24 (3-4), pp. 278-290; Costantino, N., Dotoli, M., Falagario, M., Fanti, M.P., Mangini, A.M., Sciancalepore, F., Ukovich, W., A hierarchical optimization technique for the strategic design of distribution networks (2013) Computers & Industrial Engineering, 68, pp. 849-864; Costantino, N., Dotoli, M., Falagario, M., Sciancalepore, F., Balancing the additional costs of purchasing and the vendor set dimension to reduce public procurement costs (2012) Journal of Purchasing and Supply Management, 18, pp. 189-198; Degraeve, Z., Roodhooft, F., Effectively selecting suppliers using total cost of ownership (1999) Journal of Supply Chain Management, 35 (1), pp. 5-10; Demirtas, E.A., Üstün, O., An integrated multi-objective decision making process for supplier selection and order allocation (2008) Omega-International Journal of Management Science, 36 (1), pp. 76-90; Dotoli, M., Falagario, M., A hierarchical model for optimal supplier evaluation and selection in multiple sourcing contexts (2012) International Journal of Production Research, 11, pp. 1-15; Doyle, J.R., Green, R.H., Cross-evaluation in DEA: improving discrimination among DMUs (1995) Information Systems and Operational Research, 33 (3), pp. 205-222; Duan, G., Wang, J., Liu, N., Canonical correlation discriminant analysis in the selection of suppliers (2010) Proceedings of 2010 International Conference on Artificial Intelligence and Education (ICAIE), pp. 441-444. , Hangzhou, China; Dulmin, R., Mininno, V., Supplier selection using a multi-criteria decision aid method (2003) Journal of Purchasing & Supply Management, 9, pp. 177-187; Dyson, R.G., Shale, E.A., Data envelopment analysis, operational research and uncertainty (2010) Journal of the Operational Research Society, 61, pp. 25-34; Ellram, L., Total cost of ownership: elements and implementation (1993) Journal of Supply Chain Management, 29 (4), pp. 2-11; Emrouznejad, A., Parker, B.R., Tavares, G., Evaluation of research in efficiency and productivity: a survey and analysis of the first 30 years of scholarly literature in DEA (2008) Socio-Economic Planning Sciences, 42, pp. 151-157; Falagario, M., Sciancalepore, F., Costantino, N., Pietroforte, R., Using a DEA-cross efficiency approach in public procurement tenders (2012) European Journal of Operational Research, 218, pp. 523-529; Ghodsypour, S.H., O'Brien, C., A decision support system for supplier selection using an integrated analytic hierarchy process and linear programming (1998) International Journal of Production Economics, 56-57, pp. 199-212; Ha, S.H., Krishnan, R., A hybrid approach to supplier selection for the maintenance of a competitive supply chain (2008) Expert Systems with Applications, 34 (2), pp. 1303-1311; Hatami-Marbini, A., Emrouznejad, A., Tavana, M., A taxonomy and review of the fuzzy data envelopment analysis literature: two decades in the making (2011) European Journal of Operational Research, 214, pp. 457-472; Ho, W., Xu, X., Dey, P.K., Multi-criteria decision making approaches for supplier evaluation and selection: a literature review (2010) European Journal of Operational Research, 202 (1), pp. 16-24; Jakhar, S.K., Barua, M.K., An integrated model of supply chain performance evaluation and decision-making using structural equation modelling and fuzzy AHP (2013) Production Planning & Control, 25 (11), pp. 938-957; Kao, C., Liu, T.-S., Stochastic data envelopment analysis in measuring the efficiency of Taiwan commercial banks (2009) European Journal of Operational Research, 196, pp. 312-322; Kneip, A., Simar, L., Wilson, P.W., Asymptotics and consistent bootstraps for DEA estimators in non-parametric frontier models (2008) Econometric Theory, 24 (6), pp. 1663-1697; Kuo, R.J., Wang, Y.C., Tien, F.C., Integration of artificial neural network and MADA methods for green supplier selection (2010) Journal of Cleaner Production, 18 (12), pp. 1161-1170; Law, A.M., Kelton, W.D., (2000) Simulation Modeling and Analysis, , 4th edn. McGraw-Hill, New York; Li, L., Zabinsky, Z.B., Incorporating uncertainty into a supplier selection problem (2011) International Journal of Production Economics, 134, pp. 344-356; Li, S., Murat, A., Huang, W., Selection of contract suppliers under price and demand uncertainty in a dynamic market (2009) European Journal of Operational Research, 198, pp. 830-847; Ma, R., Yao, L., Jin, M., Ren, P., The DEA game cross-efficiency model for supplier selection problem under competition (2014) Appl. Math, 8 (2), pp. 811-818; Petersen, K.J., Handfield, R.B., Ragatz, G.L., Supplier integration into new product development: coordinating product, process and supply chain design (2005) Journal of Operations Management, 23 (3-4), pp. 371-388; Petroni, A., Braglia, M., Vendor selection using principal component analysis (2000) Journal of Supply Chain Management, 36 (2), pp. 63-69; Ramanathan, R., Supplier selection problem: integrating DEA with the approaches of total cost of ownership and AHP (2007) Supply Chain Management, 12 (4), pp. 258-261; Ramsay, J., Wilson, I., Sourcing/contracting strategy selection (1990) International Journal of Operations & Production Management, 10 (8), pp. 19-28; Saaty, T.L., (1980) The Analytic Hierarchy Process, , McGraw-Hill, New York; Saaty, T.L., (1996) Decision Making with Dependence and Feedback: The Analytic Network Process, , RWS Publications, Pittsburgh, PA; Saen, R.F., Suppliers selection in the presence of both cardinal and ordinal data (2007) European Journal of Operational Research, 183 (2), pp. 741-747; Saen, R.F., Supplier selection by the new AR-IDEA model (2008) International Journal of Advanced Manufacturing Technology, 39, pp. 1061-1070; Sawik, T., Single vs. multiple objective supplier selection in a make to order environment (2010) Omega, 38 (3-4), pp. 203-212; Sexton, T.R., Silkman, R.H., Hogan, A.J., Data envelopment analysis: critique and extensions (1986) Measuring Efficiency: An Assessment of Data Envelopment Analysis, , In Silkman, R.H. (ed.), Jossey-Bass, San Francisco, CA; Swift, C.O., Preferences for single sourcing and supplier selection criteria (1995) Journal of Business Research, 32, pp. 105-111; Talluri, S., Narasimhan, R., A note on a methodology for supply base optimization (2005) IEEE Transactions on Engineering Management, 52 (1), pp. 130-139; Tauchmann, H., (2011) Orderalpha: nonparametric order-α efficiency analysis for Stata. German Stata Users' Group Meetings 2011, , University of Bamberg, Germany, Stata Users Group; Treleven, M., Schweikhart, S.B., A risk/benefit analysis of sourcing strategies: single vs. multiple sourcing (1988) Journal of Operations Management, 7 (3-4), pp. 93-114; Ufuk Bilsel, R., Ravindran, A., A multiobjective chance constrained programming model for supplier selection under uncertainty (2011) Transportation Research Part B, 45, pp. 1284-1300; Verma, R., Pullman, M.E., An analysis of the supplier selection process (1998) Omega-International Journal of Management Science, 26 (6), pp. 739-750; Vose, D., (2008) Risk Analysis: A Quantitative Guide, , 2nd edn. John Wiley and Sons, Hoboken, NJ; Weber, C.A., Desai, A., Determination of paths to vendor market efficiency using parallel coordinates representation: a negotiation tool for buyers (1996) European Journal of Operational Research, 90, pp. 142-155; Wong, W.P., Jaruphongsa, W., Lee, L.H., Supply chain performance measurement system: a Monte Carlo DEA-based approach (2008) International Journal of Industrial and Systems Engineering, 3, pp. 162-188; Zhang, X., Zhang, L., Supplier selection and purchase problem with fixed cost and constrained order quantities under stochastic demand (2011) International Journal of Production Economics, 129 (1), pp. 1-7; Zhao, K., Yu, X., A case based reasoning approach on supplier selection in petroleum enterprises (2011) Expert Systems with Applications, 38, pp. 6839-6847; Zhu, J., Imprecise data envelopment analysis (IDEA): a review and improvement with an application (2003) European Journal of Operational Research, 144 (3), pp. 513-529; Zimmermann, H.-J., (2001) Fuzzy Set Theory and Its Applications, , 4th edn. Kluwer Academic, Dordrecht; Zohrehbandian, M., Sadeghi Gavgani, S., Cross-efficiency evaluation under the principle of rank priority of DMUs (2013) World Applied Sciences Journal, 21, pp. 46-49}, document_type={Article}, source={Scopus}, }`

- Cavone, G., Dotoli, M. & Seatzu, C. (2016) Resource planning of intermodal terminals using timed Petri nets IN 2016 13th International Workshop on Discrete Event Systems, WODES 2016., 44-50.

[Bibtex]`@CONFERENCE{Cavone201644, author={Cavone, G. and Dotoli, M. and Seatzu, C.}, title={Resource planning of intermodal terminals using timed Petri nets}, journal={2016 13th International Workshop on Discrete Event Systems, WODES 2016}, year={2016}, pages={44-50}, doi={10.1109/WODES.2016.7497824}, art_number={7497824}, note={cited By 6}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84981302932&doi=10.1109%2fWODES.2016.7497824&partnerID=40&md5=6e380dff1ab12bf5ebd7b38ef41fe0ac}, abstract={In this paper we show how timed Petri nets can be efficiently used to solve problems related to resource planning in intermodal freight transport terminals. In particular, the tackled issues regard the strategic planning of the number of facilities used to transfer the intermodal transport units and the capacity/frequency of the transportation means. A real case study is considered, namely a rail-road terminal located in southern Italy. Monte Carlo simulations based on the timed Petri net model of the terminal are carried out considering various scenarios, including both the regular behavior based on real data, and situations of potential congestion resulting from increase in the commercial flows. © 2016 IEEE.}, keywords={Discrete event simulation; Freight transportation; Intelligent systems; Monte Carlo methods; Petri nets; Resource allocation; Scheduling algorithms; Traffic control, Behavior-based; Intermodal freight transport; Intermodal terminals; Intermodal transport; Rail-road terminals; Resource planning; Southern Italy; Timed Petri Net, Intermodal transportation}, references={Dotoli, M., Fanti, M.P., Mangini, A.M., Stecco, G., Ukovich, W., The impact of ICT on intermodal transportation systems: A modelling approach by Petri nets (2010) Contr. Eng. Pract, 18 (8), pp. 893-903; Caris, A., Macharis, C., Janssens, G.K., Decision support in intermodal transport: A new research agenda (2013) Comput. Ind, 64 (2), pp. 105-112; Harris, I., Wang, Y.L., Wang, H.Y., ICT in multimodal transport and technological trends: Unleashing potential for the future (2015) Int. J. Prod. Econ, 159, pp. 88-103; Perego, A., Perotti, S., Mangiaracina, R., ICT for logistics and freight transportation: A literature review and research agenda (2011) Int. J. Phys. Distrib. Logist. Manag, 4 (5), pp. 457-483; Alicke, K., Modeling and optimization of the intermodal terminal Mega Hub (2002) OR Spectrum, 24, pp. 1-17; Boschian, V., Dotoli, M., Fanti, M.P., Iacobellis, G., Ukovich, W., A metamodelling approach to the management of intermodal transportation networks (2011) IEEE Trans. Autom. Sci. Eng, 8 (3), pp. 457-469; Dotoli, M., Fanti, M.P., Iacobellis, G., Mangini, A.M., A first order hybrid Petri net model for supply chain management (2009) IEEE Trans. Autom. Sci. Eng, 6 (4), pp. 744-758; Chen, H., Amodeo, L., Feng, C., Labadi, K., Modeling and performance evaluation of supply chains using batch deterministic and stochastic Petri nets (2005) IEEE Trans. Autom. Sci. Eng, 2 (2), pp. 132-144; Giua, A., Seatzu, C., Modeling and supervisory control of railway networks using Petri nets (2008) IEEE Trans. Autom. Sci. Eng, 5 (3), pp. 431-445; Dotoli, M., Epicoco, N., Falagario, M., Cavone, G., A timed Petri nets model for performance evaluation of intermodal freight transport terminals (2015) IEEE Trans. Aut. Sci. Eng, (99), pp. 1-16. , vol.PP; Liu, C.I., Ioannou, P.A., Petri Net modeling and analysis of automated container terminal using Automated Guided Vehicle systems (2002) Transp. Res. Rec, 1782, pp. 73-83; Dotoli, M., Fanti, M.P., A coloured Petri net model for automated storage and retrieval systems serviced by rail-guided vehicles: A control perspective (2005) Int. J. Comp. Integ. Manuf, 18 (2-3), pp. 122-136; Filipova, K., Stojadinova, T., Hadjiatanasova, V., Application of Petri Nets for transport streams modeling (2002) Facta Universitatis: Architecture and Civil Engineering, 2 (4), pp. 295-306; Silva, C.A., Guedes Soares, C., Signoret, J.P., Intermodal terminal cargo handling simulation using Petri nets with predicates (2014) J. Eng. Marit. Env; Cavone, G., Dotoli, M., Seatzu, C., Management of Intermodal Freight Terminals by First-Order Hybrid Petri Nets (2016) Rob. Aut. Lett, 1 (1), pp. 2-9. , Jan; David, R., Alla, H., (2005) Discrete Continuous, and Hybrid Petri Nets, , Berlin Heidelberg, Springer-Verlag; Zimmermann, A., (2008) Stochastic Discrete Event Systems: Modeling, Evaluation, Applications, , Springer; Law, A.L., (2007) Simulation Modeling & Analysis, , New York: McGraw Hill; Sessego, F., Giua, A., Seatzu, C., HYPENS: A Matlab tool for timed discrete, continuous and hybrid Petri nets (2008) Lecture Notes in Computer Science, 5062, pp. 419-428. , Springer-Verlag}, document_type={Conference Paper}, source={Scopus}, }`

- Dotoli, M., Epicoco, N., Falagario, M. & Cavone, G. (2016) A Timed Petri Nets Model for Performance Evaluation of Intermodal Freight Transport Terminals. IN IEEE Transactions on Automation Science and Engineering, 13.842-857.

[Bibtex]`@ARTICLE{Dotoli2016842, author={Dotoli, M. and Epicoco, N. and Falagario, M. and Cavone, G.}, title={A Timed Petri Nets Model for Performance Evaluation of Intermodal Freight Transport Terminals}, journal={IEEE Transactions on Automation Science and Engineering}, year={2016}, volume={13}, number={2}, pages={842-857}, doi={10.1109/TASE.2015.2404438}, art_number={7057695}, note={cited By 38}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84929017877&doi=10.1109%2fTASE.2015.2404438&partnerID=40&md5=04520d65c7c1306a86a7b871f538bcae}, abstract={This paper presents a general modeling framework for Intermodal Freight Transport Terminals (IFTTs). The model allows simulating and evaluating the performance of such key elements of the intermodal transportation chain. Hence, it may be used by the decision maker to identify the IFTT bottlenecks, as well as to test different solutions to improve the IFTT dynamics. The proposed modeling framework is modular and based on timed Petri Nets (PNs), where places represent resources and capacities or conditions, transitions model inputs, flows, and activities into the terminal and tokens are intermodal transport units or the means on which they are transported. The model is able to represent the different types of existing IFTTs. Its effectiveness is tested first on an example from the literature and then on a real case study, the railroad inland terminal of a leading Italian intermodal logistics company, showing its ease of application. In the real case study, using the proposed formalism we test the as-is IFTT performance and evaluate alternative possible to-be improvements in order to identify and eliminate emerging criticalities in the terminal dynamics. © 2015 IEEE.}, author_keywords={Discrete-event systems; intermodal freight transport; modeling; performance evaluation; simulation; timed Petri nets}, keywords={Decision making; Freight transportation; Petri nets, Decision makers; Inland Terminals; Intermodal freight transport; Intermodal transport; Logistics company; Model framework; Timed Petri Net; Timed Petri nets models, Intermodal transportation}, references={Alessandri, A., Cervellera, C., Cuneo, M., Gaggero, M., Soncin, G., Modeling and feedback control for resource allocation and performance analysis in container terminals (2008) IEEE Trans. Intell. Transp. Syst., 9 (4), pp. 601-614; Alessandri, A., Cervellera, C., Cuneo, M., Gaggero, M., Soncin, G., Management of logistics operations in intermodal terminals by using dynamic modelling and nonlinear programming (2009) Marit. Econ. Logist., 11 (1), pp. 58-76; Alicke, K., Modeling and optimization of the intermodal terminal Mega Hub (2002) OR Spectrum, 24, pp. 1-17; Baldassarra, A., Impastato, S., Ricci, S., Intermodal terminal simulation for operations management (2010) Eur. Transp., 46, pp. 86-99; Bielli, M., Boulmakoul, A., Rida, M., Object oriented model for container terminal distributed simulation (2006) Eur. J. Oper. Res., 175 (3), pp. 1731-1751; Bontekoning, Y.M., Macharis, C., Trip, J.J., Is a new applied transportation research field emerging? A review of intermodal rail-truck freight transport literature (2004) Transp. Res., 38, pp. 1-34; Boschian, V., Dotoli, M., Fanti, M.P., Iacobellis, G., Ukovich, W., A metamodelling approach to the management of intermodal transportation networks (2011) IEEE Trans. Autom. Sci. Eng., 8 (3), pp. 457-469. , Jul; Caballini, C., Pasquale, C., Sacone, S., Siri, S., An event-triggered receding-horizon scheme for planning rail operations in maritime terminals (2014) IEEE Trans. Intell. Transp. Syst., 15 (1), pp. 365-375; Canonaco, P., Legato, P., Mazza, R.M., Musmanno, R., A queuing network model for the management of berth crane operations (2008) Comput. Oper. Res., 35 (8), pp. 2432-2446; Caris, A., Macharis, C., Janssens, G.K., Planning problems in intermodal freight transport: Accomplishments and prospects (2008) Transport. Plan. Techn., 31 (3), pp. 277-302; Caris, A., Janssens, G.K., Macharis, C., Modelling complex intermodal freight flows (2009) System Complexity to Emergent Properties, Understanding Complex Systems, pp. 291-300. , M. A. Aziz-Alaoui and C. Bertelle, Eds. Berlin, Germany: Springer; Caris, A., Macharis, C., Janssens, G.K., Decision support in intermodal transport: A new research agenda (2013) Comput. Ind., 64 (2), pp. 105-112; Cartení, A., De Luca, S., Tactical and strategic planning for a container terminal: Modelling issues within a discrete event simulation approach (2012) Simul. Modelling Pract. Theory, 21 (1), pp. 123-145; Chen, H., Amodeo, L., Feng, C., Labadi, K., Modeling and performance evaluation of supply chains using batch deterministic and stochastic Petri nets (2005) IEEE Trans. Autom. Sci. Eng., 2 (2), pp. 132-144. , Apr; David, R., Alla, H., (2005) Discrete, Continuous, and Hybrid Petri Nets, , Berlin, Germany: Springer-Verlag; Degano, C., Di Febbraro, A., On using Petri net to detect faulty behaviours in an intermodal container terminal (2002) Proc. 9th Meeting of the Euro Working Group on Transportation, "intermodality, Sustainability and Intelligent Transportation Systems", , Bari, Italy; DeWitt, W., Clinger, J., Intermodal freight transportation (2000) Transportation Research Board-Transportation in the New Millenium: State of the Art; Di Febbraro, A., Porta, G., Sacco, N., A Petri net modelling approach of intermodal terminals based on Metrocargo system (2006) Proc. 9th Intell. Transp. Syst. Conf., pp. 1442-1447. , Toronto, ON, Canada; Dotoli, M., Epicoco, N., Falagario, M., Cavone, G., A timed Petri nets model for intermodal freight transport terminals (2014) Proc. 12th Int. Works. Discr. Event Syst. (WODES'14), pp. 176-181. , Paris-Cachan, France; Dotoli, M., Epicoco, N., Falagario, M., Cavone, G., Turchiano, B., Simulation and performance evaluation of an intermodal terminal using Petri nets (2014) Proc. Int. Conf. Contr., Dec. Inform. Technol. (CoDIT'14), pp. 327-332. , Metz, France; Dotoli, M., Fanti, M.P., A coloured Petri net model for automated storage and retrieval systems serviced by rail-guided vehicles: A control perspective (2005) Int. J. Comp. Integr. Manuf., 18 (2-3), pp. 122-136; Dotoli, M., Fanti, M.P., Iacobellis, G., Mangini, A.M., A first order hybrid Petri net model for supply chain management (2009) IEEE Trans. Autom. Sci. Eng., 6 (4), pp. 744-758. , Oct; Dotoli, M., Fanti, M.P., Mangini, A.M., Stecco, G., Ukovich, W., The impact of ICT on intermodal transportation systems: A modelling approach by Petri nets (2010) Contr. Eng. Pract., 18 (8), pp. 893-903; Dotoli, M., Hammadi, S., Jeribi, K., Russo, C., Zgaya, H., A multiagent decision support system for optimization of co-modal transportation route planning services (2013) Proc. 52nd IEEE Conf. Decision Control, , Florence, Italy; Dotoli, M., Epicoco, N., Falagario, M., Palma, D., Turchiano, B., A train load planning optimization model for intermodal freight transport terminals: A case study (2013) Proc. IEEE Conf. Systems, Man and Cybernetics, , Manchester, U.K; Dotoli, M., Epicoco, N., Falagario, M., Seatzu, C., Turchiano, B., An optimization technique for intermodal rail-road freight transport terminals (2014) Proc. IEEE Int. Conf. Robot. Autom., 6p. , Hong Kong, China; Du, Y., Tan, W., Zhou, M., Timed compatibility analysis of web service composition: A modular approach based on Petri nets (2014) IEEE Trans. Autom. Sci. Eng., 11 (2), pp. 594-606. , Apr; (2011) White Paper on Transport-Roadmap to A Single European Transport Area EU; Filipova, K., Stojadinova, T., Hadjiatanasova, V., Application of Petri Nets for transport streams modeling (2002) Facta Universitatis: Architecture and Civil Engineering, 2 (4), pp. 295-306; Gambardella, L.M., Mastrolilli, M., Rizzoli, A.E., Zaffalon, M., An optimization methodology for intermodal terminal management (2001) J. Intelligent Manufacturing, 12 (5-6), pp. 521-534; Giua, A., DiCesare, F., Silva, M., Generalized Mutual Exclusion Constraints on nets with uncontrollable transitions (1992) Proc. Int. Conf. Syst. Man Cybern., pp. 974-979. , Chicago, IL, USA; Giua, A., Seatzu, C., Modeling and supervisory control of railway networks using Petri nets (2008) IEEE Trans. Autom. Sci. Eng., 5 (3), pp. 431-445. , Jul; Günther, H.O., Kim, K.H., Container terminals and terminal operations (2006) OR Spectrum, 28, pp. 437-445; Harris, I., Wang, Y.L., Wang, H.Y., ICT in multimodal transport and technological trends: Unleashing potential for the future (2015) Int. J. Prod. Econ., 159, pp. 88-103; Kim, W.-S., Kim, J.H., Kim, H., Kwon, H., Morrison, J.R., Capacity and queueing evaluation of port systems with offshore container unloading (2010) Proc. Int. Conf. Logist.Marit. Sys., , Busan, Korea; Law, A.L., (2007) Simulation Modeling & Analysis, , New York, NY, USA: McGraw-Hill; Lee, C., Huang, H.C., Liu, B., Xu, Z., Development of timed Colour Petri net simulation models for air cargo terminal operations (2006) Comput. Ind. Eng., 51 (1), pp. 102-110; Liu, C.I., Ioannou, P.A., Petri Net modeling and analysis of automated container terminal using Automated Guided Vehicle systems (2002) Transp.. Res. Rec., 1782, pp. 73-83; Liu, C.I., Jula, H., Ioannou, P.A., Design, simulation, and evaluation of automated container terminals (2002) IEEE Trans. Intell. Transp. Syst., 3 (1), pp. 12-26. , Mar; Maione, G., Ottomanelli, M., A Petri net model for simulation of container terminal operations (2005) Advanced or and AI Methods in Transportation, pp. 373-378. , A. Jaszkiewicz, Ed. et al. Poznan, Poland: House of Poznan Uni. Technol; Murty, K.G., Liu, J., Wan, Y.W., Linn, R., A decision support system for operations in a container terminal (2005) Decision Supp. Syst., 39 (3), pp. 309-332; Parola, F., Sciomachen, A., Intermodal container flows in a port system network: Analysis of possible growths via simulation models (2005) Int. J. Prod. Econ., 97 (1), pp. 75-88; Perego, A., Perotti, S., Mangiaracina, R., ICT for logistics and freight transportation: A literature review and research agenda (2011) Int. J. Phys. Distrib. Logist. Manag., 4 (5), pp. 457-483; Peterson, J.L., (1981) Petri Net Theory and the Modeling of Systems, , Pearson, Ed. Englewood Cliffs, NJ, USA: Prentice-Hall; Rizzoli, A.E., Fornara, N., Gambardella, L.M., A simulation tool for combined rail/road transport in intermodal terminals (2002) Math. Comput. Simulat., 59 (1-3), pp. 57-71; Sessego, F., Giua, A., Seatzu, C., HYPENS: A Matlab tool for timed discrete, continuous and hybrid Petri nets (2008) Lecture Notes in Computer Science, 5062, pp. 419-428. , Berlin, Germany: Springer-Verlag; Silva, C.A., Soares, C.G., Signoret, J.P., Intermodal terminal cargo handling simulation using Petri nets with predicates (2014) J. Eng. Marit. Env.; Stahlbock, R., Voss, S., Operations research at container terminals: A literature update (2008) OR Spectrum, 30, pp. 1-52; Steenken, D., Voß, S., Stahlbock, R., Container terminal operation and operations research-A classification and literature review (2004) OR Spectrum, 26, pp. 3-49; (2001) Terminology on Combined Transport, , New York and Geneva, United Nations; Vis, I.F.A., De Koster, R.D., Transshipment of containers at a container terminal: An overview (2003) Eur. J. Oper. Res., 147 (1), pp. 1-16; Viswanadham, N., (1999) Analysis of Manufacturing Enterprises: An Approach to Leveraging Value Delivery Processes for Competitive Advantage, , Boston, MA, USA: Kluwer Academic; Zimmermann, A., (2008) Stochastic Discrete Event Systems: Modeling, Evaluation, Applications, , New York, NY, USA: Springer}, document_type={Article}, source={Scopus}, }`

- Cavone, G., Dotoli, M. & Seatzu, C. (2016) Management of Intermodal Freight Terminals by First-Order Hybrid Petri Nets. IN IEEE Robotics and Automation Letters, 1.2-9.

[Bibtex]`@ARTICLE{Cavone20162, author={Cavone, G. and Dotoli, M. and Seatzu, C.}, title={Management of Intermodal Freight Terminals by First-Order Hybrid Petri Nets}, journal={IEEE Robotics and Automation Letters}, year={2016}, volume={1}, number={1}, pages={2-9}, doi={10.1109/LRA.2015.2502905}, art_number={7339445}, note={cited By 12}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85058585244&doi=10.1109%2fLRA.2015.2502905&partnerID=40&md5=5a498586aaee188bb2339dd6fff680ce}, abstract={In this paper, we show how first-order hybrid Petri nets can be efficiently used to model and manage intermodal freight transport terminals. The proposed formalism enables the terminal decision maker to choose the speeds associated with continuous transitions in order to optimize the terminal performance by two alternative control policies: the container flows maximization and the minimization of the residual containers in the storage area. The approach may be used either offline, to take decisions on the terminal resources, or online, to solve congestions/malfunctions. A real case study is modeled and managed by the proposed optimal control policies. © 2016 IEEE.}, author_keywords={Discrete Event Dynamic Automation Systems; Logistics; Petri Nets for Automation Control}, keywords={Automation; Bottling plants; Containers; Decision making; Logistics; Petri nets, Automation controls; Automation systems; Continuous transitions; Decision makers; First-order hybrid Petri nets; Intermodal freight; Intermodal freight transport; Optimal control policy, Freight transportation}, references={Harris, I., Wang, Y.L., Wang, H.Y., ICT in multimodal transport and technological trends: Unleashing potential for the future (2015) Int. J. Prod. Econ., 159, pp. 88-103; Boschian, V., Dotoli, M., Fanti, M.P., Iacobellis, G., Ukovich, W., A metamodelling approach to the management of intermodal transportation networks (2011) IEEE Trans. Autom. Sci. Eng., 8 (3), pp. 457-469. , Jul; Dotoli, M., Epicoco, N., Falagario, M., Cavone, G., A timed Petri nets model for performance evaluation of intermodal freight transport terminals (2015) IEEE Trans. Autom. Sci. Eng., pp. 1-16. , Mar; Chen, H., Amodeo, L., Feng, C., Labadi, K., Modeling and performance evaluation of supply chains using batch deterministic and stochastic Petri nets (2005) IEEE Trans. Autom. Sci. Eng., 2 (2), pp. 132-144. , Apr; Dotoli, M., Fanti, M.P., Iacobellis, G., Mangini, A.M., A firstorder hybrid Petri net model for supply chain management (2009) IEEE Trans. Autom. Sci. Eng., 6 (4), pp. 744-758. , Oct; Giua, A., Seatzu, C., Modeling and supervisory control of railway networks using Petri nets (2008) IEEE Trans. Autom. Sci. Eng., 5 (3), pp. 431-445. , Jul; Dotoli, M., Fanti, M.P., Mangini, A.M., Stecco, G., Ukovich, W., The impact of ICT on intermodal transportation systems: A modelling approach by Petri nets (2010) Contr. Eng. Pract., 18 (8), pp. 893-903; Liu, C.I., Ioannou, P.A., Petri net modeling and analysis of automated container terminal using automated guided vehicle systems (2002) Transp. Res. Rec., 1782, pp. 73-83; Dotoli, M., Fanti, M.P., An urban traffic network model via coloured timed Petri nets (2006) Contr. Eng. Pract., 14, pp. 1213-1229; Silva, C.A., Guedes Soares, C., Signoret, J.P., Intermodal terminal cargo handling simulation using Petri nets with predicates (2014) J. Eng. Marit. Environ.; Di Febbraro, A., Giglio, D., Sacco, N., Urban traffic control structure based on hybrid Petri nets (2004) IEEE Trans. Intell. Transp. Syst., 5 (4), pp. 224-237. , Dec; Balduzzi, F., Giua, A., Menga, G., First-order hybrid Petri nets: A model for optimization and control (2000) IEEE Trans. Robot. Autom., 16 (4), pp. 382-399. , Aug; Balduzzi, F., Di Febbraro, A., Combining fault detection and process optimization in manufacturing systems using first-order hybrid Petri nets (2001) Proc. IEEE Int. Conf. Robot. Autom., 1, pp. 40-45; Balduzzi, F., Giua, A., Seatzu, C., Modelling and simulation of manufacturing systems with first-order hybrid Petri nets (2001) Int. J. Prod. Res., 39 (2), pp. 255-282; Kim, Y.W., Inaba, A., Suzuki, T., Okuma, S., Realization of fault tolerant manufacturing system and its scheduling based on hierarchical Petri net modeling (2003) Proc. IEEE Int. Conf. Robot. Autom., 3, pp. 3959-3964; Dotoli, M., Fanti, M.P., Iacobellis, G., A freeway traffic control model by first order hybrid Petri nets (2011) Proc. IEEE Conf. Autom. Sci. Eng., pp. 425-431; Fanti, M.P., Iacobellis, G., Mangini, A.M., Ukovich, W., Freeway traffic modeling and control in a first-order hybrid Petri net framework (2014) IEEE Trans. Autom. Sci. Eng., 11 (1), pp. 90-102. , Jan; Sessego, F., (2008) HYPENS, , http://www.diee.unica.it/automatica/hypens/, [Online]}, document_type={Article}, source={Scopus}, }`

### 2015

- Dotoli, M., Epicoco, N., Falagario, M., Angelico, B. & Vinciullo, A. (2015) A two-step optimization model for the pre- and end-haulage of containers at intermodal freight terminals IN 2015 European Control Conference, ECC 2015., 3472-3477.

[Bibtex]`@CONFERENCE{Dotoli20153472, author={Dotoli, M. and Epicoco, N. and Falagario, M. and Angelico, B. and Vinciullo, A.}, title={A two-step optimization model for the pre- and end-haulage of containers at intermodal freight terminals}, journal={2015 European Control Conference, ECC 2015}, year={2015}, pages={3472-3477}, doi={10.1109/ECC.2015.7331071}, art_number={7331071}, note={cited By 5}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84963799902&doi=10.1109%2fECC.2015.7331071&partnerID=40&md5=025d5a96d0a749c51da62cd89b09b4fe}, abstract={The paper focuses on the optimization of containers pre- and end-haulage by road in intermodal terminals, which is one of the most critical factors in door to door transport effectiveness and profit. We present an optimization model that allows solving in an exact and optimal way the vehicle routing and fleet management problems. The model comprises two steps: first it optimizes the distance traveled by road, enabling to match a delivery with a pick up; the second step is devoted to minimizing the number of vehicles required for the deliveries, while satisfying the routes obtained from the previous step. The proposed optimization model is applied to a real case study to test its effectiveness. © 2015 EUCA.}, keywords={Containers; Fleet operations; Roads and streets, Critical factors; Fleet management; Intermodal freight; Intermodal terminals; Minimizing the number of; Optimization modeling; Real case; Two-step optimizations, Optimization}, references={Archetti, C., Speranza, M.G., Hertz, A., A tabu search algorithm for the split delivery vehicle routing problem (2003) Transp Sci, 40 (1), pp. 64-73; Arunapuram, S., Mathur, K., Solow, D., Vehicle routing and scheduling with full truckloads (2003) Transp Sci, 37 (2), pp. 170-182; Boschian, V., Dotoli, M., Fanti, M.P., Iacobellis, G., Ukovich, W., A metamodelling approach to the management of intermodal transportation networks (2011) IEEE Trans Aut Sci Eng, 8 (3), pp. 457-469; Caballini, C., Sacone, S., Saeednia, M., A decomposition approach for optimizing trucks trips for a single carrier (2013) Proc. 16th Int IEEE Conf on Intelligent Transportation Systems (ITSC), , The Hague, The Netherlands, October, 6-9; Caris, A., Janssens, G., A local search heuristic for the pre-and end-haulage of intermodal container terminals (2009) Comput Oper Res, 36 (10), pp. 2763-2772; Dantzig, G.B., Ramser, J.H., The truck dispatching problem (1959) Management Science, 6 (1), pp. 80-91; Dejax, P.J., Crainic, T.G., A review of empty flows and Fleet Management models in freight transportation (1987) Transp Sci, 21 (4), pp. 227-248; Desrochers, M., Lenstraa, J.K., Savelsbergh, M.W.P., A classification scheme for vehicle routing and scheduling problems (1990) Eur J Oper Res, 46 (3), pp. 322-332; Dotoli, M., Epicoco, N., Falagario, M., Cavone, G., Turchiano, B., Simulation and performance evaluation of an intermodal terminal using Petri nets (2014) 2nd Int Conf Control Decision and Information Technologies (CODIT 2014), , Metz, France, November, 3-5; Dotoli, M., Epicoco, N., Falagario, M., Cavone, G., A Timed Petri Nets model for intermodal freight transport terminals (2014) Proc. 12th IFAC Int. Work. Discrete Event Systems (WODES14), pp. 176-181. , Paris-Cachan, France, May 14-16; Dotoli, M., Epicoco, N., Falagario, M., Seatzu, C., Turchiano, B., Optimization of intermodal rail-road freight transport terminals (2014) Proc. 2014 IEEE Int. Conf. Robotics and Automation (ICRA 2014), pp. 1971-1976. , Hong Kong (China), May 31-June 7; Dotoli, M., Epicoco, N., Falagario, M., Cavone, G., A Timed Petri Nets model for performance evaluation of intermodal freight transport terminals (2015) IEEE Trans. Autom. Sci. Eng., , in press; Dotoli, M., Fanti, M.P., Mangini, A.M., Stecco, G., Ukovich, W., The impact of ICT on intermodal transportation systems: A modelling approach by Petri nets (2010) Contr Eng Pract, 18 (8), pp. 893-903; Dotoli, M., Sciancalepore, F., Epicoco, N., Falagario, M., Turchiano, B., Costantino, N., A periodic event scheduling approach for offline timetable optimization of regional railways (2013) Proc. 10th IEEE Int Conf Networking, Sensing and Control (ICNSC 2013), , Paris, France, April, 10-12; Eng-Larsson, F., Kohn, C., Modal shift for greener logistics-The shipper's perspective (2012) Int J Phys Distrib Logist Manag, 42 (1), pp. 36-59; Imai, A., Nishimura, E., Current, J., A Lagrangian relaxationbased heuristic for the vehicle routing with full container load (2007) Eur J Oper Res, 176, pp. 87-105; Jula, H., Dessouky, M., Ioannou, P., Hall, R., Full truck load assignment and route planning in deterministic and stochastic environments (2003) Proc. NSF Design Service Manufacture and Industrial Innovation Research Conference, , Birmingham, AL; Kumar, S.N., Panneerselvam, R., A survey on the Vehicle Routing Problem and its variants (2012) Intelligent Information Management, 4, pp. 66-74; Lai, M., Di Francesco, M., Zuddas, P., Heuristic for the routing of trucks with double container loads (2012) 3rd Student Conference on Operational Research, 22, pp. 84-93; Macharis, C., Bontekoning, Y.M., Opportunities for or in intermodal freight transport research: A review (2004) Eur J Oper Res, 153, pp. 400-416; Mingozzi, A., Giorgi, S., Baldacci, R., An exact algorithm for the Vehicle Routing Problem with Backhauls (1999) Transp Sci, 33 (3), pp. 315-329; Ongarj, L., Ongkunaruk, P., An integer programming for a bin packing problem with time windows: A case study of a Thai seasoning company (2013) 10th Int Conf Service Systems and Service Management (ICSSSM), , Hong Kong, China, July, 17-19; Parragh, S.N., Doerner, K.F., Hartl, R.F., A survey on Pickup and Delivery problems (2008) Journal Für Betriebswirtschaft, 58 (1), pp. 21-51; Perego, A., Perotti, S., Mangiaracina, R., ICT for logistics and freight transportation: A literature review and research agenda (2011) Int J Phys Distrib Logist Manag, 41 (5), pp. 457-483; Toth, P., Vigo, D., The vehicle routing problem (2001) Monographs on Discrete Mathematics and Applications, p. 381; Toth, P., Vigo, D., An exact algorithm for the vehicle routing problem with backhauls (1997) Transp Sci, 31 (4), pp. 372-385; Vidovi, M., Nikoli, M., Popovi, D., Two mathematical formulations for the containers drayage problem with time windows (2012) Int J Business Science and Applied Management, 7 (3), pp. 23-32; Vidovi, M., Radivojevi, G., Rakovi, B., Vehicle routing in containers Pickup and Delivery process (2011) Procedia Soc Behav Sci, 20, pp. 335-343; Wang, J.Y., (2008) Transportation and Assignment Problem, , College of Management, NCTU Operation Research I}, document_type={Conference Paper}, source={Scopus}, }`

- Dotoli, M., Epicoco, N. & Falagario, M. (2015) Integrated supplier selection and order allocation under uncertainty in agile supply chains IN IEEE International Conference on Emerging Technologies and Factory Automation, ETFA..

[Bibtex]`@CONFERENCE{Dotoli2015, author={Dotoli, M. and Epicoco, N. and Falagario, M.}, title={Integrated supplier selection and order allocation under uncertainty in agile supply chains}, journal={IEEE International Conference on Emerging Technologies and Factory Automation, ETFA}, year={2015}, volume={2015-October}, doi={10.1109/ETFA.2015.7301509}, art_number={7301509}, note={cited By 3}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84952912259&doi=10.1109%2fETFA.2015.7301509&partnerID=40&md5=0c2187c85b7474af8440d5fd9fd0f3f1}, abstract={This paper focuses on the supplier selection problem and the subsequent order allocation, extending an approach originally proposed by some of the authors for supplier ranking under uncertainty. The novel method integrates the cross-efficiency Data Envelopment Analysis and the fuzzy set theory to obtain a ranking of suppliers under nondeterministic evaluation criteria. Subsequently, a fuzzy integer linear programming model allows determining the quantities to require from each supplier as a compromise between the suppliers' efficiency, procurement costs, and time required to fulfill the order, while respecting the suppliers' capacity and satisfying the customers' demand. The case study of an SME manufacturer shows the technique effectiveness. © 2015 IEEE.}, author_keywords={agile supply chain; data envelopment analysis; fuzzy logic; manufacturing; order allocation; supplier selection; uncertainty}, keywords={Data envelopment analysis; Efficiency; Factory automation; Fuzzy logic; Fuzzy set theory; Integer programming; Manufacture; Supply chains, Agile supply chains; Cross efficiency; Evaluation criteria; Fuzzy integer linear programming; Order allocation; Procurement costs; Supplier selection; uncertainty, Uncertainty analysis}, references={Agarwal, P., Sahai, M., Mishra, V., Bag, M., Singh, V., A review of multicriteria decision making techniques for supplier evaluation and selection (2011) Int J Ind Eng Comput, 2, pp. 801-810; Aissaoui, N., Haouari, M., Hassini, E., Supplier selection and order lot sizing modeling: A review (2007) Comput Oper Res, 34, pp. 3516-3540; Amindoust, A., Ahmed, S., Saghafinia, A., Supplier selection and order allocation scenarios in supply chain: A review (2013) Eng Manag Rev, 2 (3), pp. 75-80; Angulo Meza, L., Pereira Estellita-Lins, M., Review of methods for increasing discrimination in data envelopment analysis (2002) Ann Oper Res, 116 (1-4), pp. 225-242; Charnes, A., Cooper, W.W., Rhodes, E., Measuring the efficiency of decision making units (1978) Eur J Oper Res, 2, pp. 429-444; Costantino, N., Dotoli, M., Falagario, M., Fanti, M.P., Mangini, A.M., Sciancalepore, F., Supplier selection in the public procurement sector via a data envelopment analysis approach (2011) Proc. 19th IEEE Medit Conf Control and Automation (MED 2011), , Corfu, Greece, June 23-25; Costantino, N., Dotoli, M., Falagario, M., Fanti, M.P., Mangini, A.M., Sciancalepore, F., Ukovich, W., A fuzzy programming approach for the strategic design of distribution networks (2011) Proc. 7th IEEE Int. Conf. Autom Sci Eng (CASE 2011), , Trieste (Italy), August 24-27; Costantino, N., Dotoli, M., Falagario, M., Fanti, M.P., Mangini, A.M., A model for supply management of agile manufacturing supply chains (2012) Int J Prod Econ, 135 (1), pp. 451-457; Costantino, N., Dotoli, M., Epicoco, N., Falagario, M., Sciancalepore, F., A cross efficiency fuzzy data envelopment analysis technique for supplier evaluation under uncertainty (2012) Proc. 17th IEEE Int Conf Emerging Technologies and Factory Automation (ETFA 2012), , Krakòv (Poland), September 17-21; Costantino, N., Dotoli, M., Epicoco, N., Falagario, M., Sciancalepore, F., A novel fuzzy data envelopment analysis methodology for performance evaluation in a two-stage supply chain (2012) Proc. 8th IEEE International Conference on Automation Science and Engineering (CASE 2012), , Seoul (Korea), August 20-24; Crispim, J.A., De Sousa, J.P., Partner selection in virtual enterprises: A multi-criteria decision support approach (2009) Int J Prod Res, 47 (17), pp. 4791-4812; Demirtas, E.A., Üstün, O., An integrated multiobjective decision making process for supplier selection and order allocation (2008) Omega, 36, pp. 76-90; Digiesi, S., Mossa, G., Mummolo, G., A sustainable order quantity model under uncertain product demand (2013) Manufacturing Modelling, Management, and Control, 7 (1), pp. 664-669; Dotoli, M., Falagario, M., A hierarchical model for optimal supplier selection in multiple sourcing contexts (2012) Int J Prod Res, 50 (11), pp. 2953-2967; Dotoli, M., Epicoco, N., Falagario, M., Sciancalepore, F., A cross efficiency fuzzy data envelopment analysis technique for performance evaluation of decision making units under uncertainty (2015) Comput Ind Eng, 79, pp. 103-114; Dotoli, M., Epicoco, N., Falagario, M., Sciancalepore, F., A stochastic cross-efficiency data envelopment analysis approach for supplier selection under uncertainty (2015) Int Trans Oper Res, 2, pp. 1-25; Hatami-Marbini, A., Emrouznejad, A., Tavana, M., A taxonomy and review of the fuzzy data envelopment analysis literature: Two decades in the making (2011) Eur J Oper Res, 214, pp. 457-472; Kannan, D., Khodaverdi, R., Olfat, L., Jafarian, A., Diabat, A., Integrated fuzzy multi criteria decision making method and multiobjective programming approach for supplier selection and order allocation in a green supply chain (2013) J Clean Prod, 47, pp. 355-367; Kristianto, Y., Gunasekaran, A., Helo, P., Hao, Y., A model of resilient supply chain network design: A two-stage programming with fuzzy shortest path (2014) Expert Syst Appl, 41, pp. 39-49; Li, Z., Wong, W.K., Kwong, C.K., An integrated model of material supplier selection and order allocation using fuzzy extended AHP and multiobjective programming (2013) Math Probl Eng, 2013, 14p; Liu, S.T., Chuang, M., Fuzzy efficiency measures in fuzzy DEA/AR with application to university libraries (2009) Expert Syst Appl, 36 (2), pp. 1105-1113; Matinrad, N., Roghaniana, E., Rasib, Z., Supply chain network optimization: A review of classification, models, solution techniques and future research (2013) Uncertain Supply Chain Manag, 1, pp. 1-24; Mirzapour Al-e-Hashem, S.M.J., Maleklyand, H., Aryanezhad, M.B., A multi-objective robust optimization model for multi-product multi-site aggregate production planning in a supply chain under uncertainty (2011) Int J Prod Econ, 134, pp. 28-42; Mossa, G., Digiesi, S., Rubino, S., A sustainable EOQ model for repairable spare parts under uncertain demand (2015) J Manag Math, 26 (2), pp. 185-203; Sexton, T.R., Silkman, R.H., Hogan, A.J., Data envelopment analysis: Critique and extensions (1986) Measuring Efficiency: An Assessment of Data Envelopment Analysis, , in R.H. Silkman (Ed.), San Francisco, CA: Jossey-Bass; Ting, S.C., Cho, D.I., An integrated approach for supplier selection and purchasing decisions (2008) Supply Chain Manag, 13 (2), pp. 116-127; Ware, N.R., Singh, S.P., Banwet, D.K., Supplier selection problem: A state-of-the-art review (2012) Manag Sci Lett, 2, pp. 1465-1490; Wu, C., Barnes, D., A literature review of decision-making models and approaches for partner selection in agile supply chains (2011) J Purch Supply Manag, 17, pp. 256-274; Zimmermann, H.-J., (2001) Fuzzy Set Theory and Its Applications, , Kluwer Academic Publishers, Boston/Dordrecht/London, 4th Ed}, document_type={Conference Paper}, source={Scopus}, }`

- Dotoli, M., Epicoco, N. & Seatzu, C. (2015) An improved technique for train load planning at intermodal rail-road terminals IN IEEE International Conference on Emerging Technologies and Factory Automation, ETFA..

[Bibtex]`@CONFERENCE{Dotoli2015, author={Dotoli, M. and Epicoco, N. and Seatzu, C.}, title={An improved technique for train load planning at intermodal rail-road terminals}, journal={IEEE International Conference on Emerging Technologies and Factory Automation, ETFA}, year={2015}, volume={2015-October}, doi={10.1109/ETFA.2015.7301580}, art_number={7301580}, note={cited By 5}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84952908064&doi=10.1109%2fETFA.2015.7301580&partnerID=40&md5=16668e6fa05111e7cfacf104b43ef027}, abstract={This paper presents a train load planning technique for intermodal rail-road terminals. The proposed method aims at maximizing the train commercial value while respecting priority, physical, financial, and prosecution constraints (i.e., taking into account containers that prosecute their trip after the first destination). The approach consists of two phases: 1) modifying a previous approach by some of the authors, a linear integer programming problem is solved to maximize the train commercial value, keeping into account urgencies and priorities; 2) hence, a heuristics is used to take into account prosecuting containers and reduce the number of wagons to be re-handled. The technique is tested on a real case study and compared with the previous strategy proposed by some of the authors to show its effectiveness and ease of application. © 2015 IEEE.}, author_keywords={intermodal freight transport; load planning; optimization; rail-road transport; train composition}, keywords={Containers; Factory automation; Freight transportation; Optimization; Railroads; Roads and streets; Transportation, Improved techniques; Intermodal freight transport; Linear integer programming; Load planning; Rail-road terminals; Real case; Train composition; Train loads, Integer programming}, references={Anghinolfi, D., Foti, L., Maratea, M., Paolucci, M., Siri, S., Optimal loading plan for multiple trains in container terminals (2012) Proc. 5th Int Work Freight Transport Logis, , May 21-25, Mykonos (Greece); Ambrosino, D., Bramardi, A., Pucciano, M., Sacone, S., Siri, S., Modeling and solving the train load planning problem in seaport container terminals (2011) Proc. 7th Conf Autom Sci Eng, pp. 208-213; Arnold, P., Peeters, D., Thomas, I., Modelling a rail/road intermodal transportation system (2004) Transp. Res., 40, pp. 255-270; Bontekoning, Y.M., Macharis, C., Trip, J.J., Is a new applied transportation research field emerging? A review of intermodal rail-truck freight transport literature (2004) Transp. Res., 38, pp. 1-34; Bruns, F., Knust, S., Optimized load planning of trains in intermodal transportation (2012) OR Spectrum, 34 (3), pp. 511-533; Caris, A., Macharis, C., Jansses, G.K., Planning problems in intermodal freight transport: Accomplishments and prospects (2008) Transp. Plan. Techn., 31 (3), pp. 277-302; Corry, P., Kozan, E., An assignment model for dynamic load planning of intermodal trains (2006) Comp. Oper. Res., 33, pp. 1-17; Dotoli, M., Epicoco, N., Falagario, M., Palma, D., Turchiano, B., A train load planning optimization model for intermodal freight transport terminals: A case study (2013) Proc. 2013 IEEE Int Conf Systems Man Cybernetics, pp. 3597-3602. , October 13-16, Manchester (UK); Stahlbock, R., Voss, S., Operations research at container terminals: A literature update (2008) OR Spectrum, 30, pp. 1-52}, document_type={Conference Paper}, source={Scopus}, }`

- Carli, R., Dotoli, M. & Pellegrino, R. (2015) ICT and optimization for the energy management of smart cities: The street lighting decision panel IN IEEE International Conference on Emerging Technologies and Factory Automation, ETFA..

[Bibtex]`@CONFERENCE{Carli2015, author={Carli, R. and Dotoli, M. and Pellegrino, R.}, title={ICT and optimization for the energy management of smart cities: The street lighting decision panel}, journal={IEEE International Conference on Emerging Technologies and Factory Automation, ETFA}, year={2015}, volume={2015-October}, doi={10.1109/ETFA.2015.7301435}, art_number={7301435}, note={cited By 15}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84952882893&doi=10.1109%2fETFA.2015.7301435&partnerID=40&md5=012ab885ea35346c5c280804295955c9}, abstract={The paper addresses the emerging need for tools devoted to the energy governance of smart cities. We propose a hierarchical decision process that supports the energy manager in governing the smart city while addressing different urban sectors with an integrated, structured, and transparent planning. Starting from the urban control center proposed in a previous contribution for the urban energy management, a hierarchical strategic decision structure is proposed. More in detail, a two-level decentralized programming model integrates several decision making units (decision panels), each focusing on the energy optimization of a specific urban subsystem. We focus on the presentation of the street lighting decision panel and on its application to the energy management of the public lighting of the city of Bari (Italy), where a smart city program has recently been launched. © 2015 IEEE.}, author_keywords={Cities and towns; Decision making; Lighting; Optimization; Smart cities}, keywords={Application programs; Energy management; Factory automation; Lighting; Optimization; Street lighting, Cities and towns; Decision making unit; Energy optimization; Hierarchical decisions; ITS applications; Programming models; Smart cities; Strategic decisions, Decision making}, references={Başar, T., Olsder, G.J., (1999) Dynamic Noncooperative Game Theory, , SIAM Series in Classics in Applied Mathematics. Philadelphia, PA:SIAM; Batty, M., Axhausen, K.W., Giannotti, F., Pozdnoukhov, A., Bazzani, A., Wachowicz, M., Ouzounis, G., Portugali, Y., Smart cities of the future (2012) Eur. Phys. J. Spec. Top., 214 (1), pp. 481-518; Caragliu, A., Del Bo, C., Nijkamp, P., Smart cities in Europe (2009) Proc. 3rd Centr. Europ. Conf. Reg. Sci. (CERS), , Oct; Carli, R., Deidda, P., Dotoli, M., Pellegrino, R., An urban control center for the energy governance of a smart city (2014) Proc. IEEE ETFA2014, pp. 1-7; Carli, R., Dotoli, M., Pellegrino, R., Ranieri, L., Measuring and managing the smartness of cities: A framework for classifying performance indicators (2013) Proc. IEEE SMC2013, pp. 1288-1293; Carli, R., Dotoli, M., Pellegrino, R., Ranieri, L., Using multi-objective optimization for the integrated energy efficiency improvement of a smart city public buildings' portfolio (2015) IEEE CASE2015; Figueira, J., Greco, S., Ehrgott, M., (2005) Multiple Criteria Decision Analysis: State of the Art Surveys, , Springer, Boston; Glover, F., Improved linear integer programming formulations of nonlinear integer problems (1975) Manag. Sci., 22 (4), pp. 455-460; Lagorse, J., Paire, D., Miraoui, A., Sizing optimization of a stand-alone street lighting system powered by a hybrid system using fuel cell, PV and battery (2009) Renew. Ener., 34 (3), pp. 683-691; Lust, T., Teghem, J., The multiobjective multidimensional knapsack problem: A survey and a new approach (2012) Int. Trans. Op. Res., 19 (4), pp. 495-520; Marler, R.T., Arora, J.S., Survey of multi-objective optimization methods for engineering (2004) Structur. Multidisc. Optim., 26 (6), pp. 369-395; Nam, T.W., Pardo, T.A., Conceptualizing smart city with dimensions of technology, people, and institutions (2011) Proc. 12th Int. Digit. Governm. Res. Conf.; Narisada, K., Schreuder, D., (2004) Light Pollution Handbook, 322. , Springer Science & Business Media; Rea, M.S., (2000) The IESNA Lighting Handbook, , New York: Illuminating Engineering Society of North America; Vicente, L.N., Calamai, P.H., Bilevel and multilevel programming: A bibliography review (1994) J. Glob. Optim., 5 (3), pp. 291-306}, document_type={Conference Paper}, source={Scopus}, }`

- Carli, R., Dotoli, M., Pellegrino, R. & Ranieri, L. (2015) Using multi-objective optimization for the integrated energy efficiency improvement of a smart city public buildings’ portfolio IN IEEE International Conference on Automation Science and Engineering., 21-26.

[Bibtex]`@CONFERENCE{Carli201521, author={Carli, R. and Dotoli, M. and Pellegrino, R. and Ranieri, L.}, title={Using multi-objective optimization for the integrated energy efficiency improvement of a smart city public buildings' portfolio}, journal={IEEE International Conference on Automation Science and Engineering}, year={2015}, volume={2015-October}, pages={21-26}, doi={10.1109/CoASE.2015.7294035}, art_number={7294035}, note={cited By 25}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84952780524&doi=10.1109%2fCoASE.2015.7294035&partnerID=40&md5=64470489d4914a6d949700900aa47d44}, abstract={The paper presents a multi-objective optimization algorithm to improve in an integrated and holistic way the building stock energy efficiency, sustainability, and comfort, while efficiently allocating the available budget to the buildings. The developed algorithm determines a set of optimal energy retrofit plans for a portfolio of public buildings in a smart city. An existing stock of public buildings located in the municipality of Bari, Italy is used as case study. The application results demonstrate that the developed algorithm is an effective support tool for the smart city governance in enhancing the energy efficiency performance of a stock of public buildings. © 2015 IEEE.}, keywords={Algorithms; Automation; Budget control; Buildings; Multiobjective optimization; Optimization, Building stocks; Energy efficiency improvements; Optimal energy; Public buildings; Smart cities; Support tool, Energy efficiency}, references={Asadi, E., Gameiro Da Silva, M., Henggeler Antunes, C., Dias, L., Multi-objective optimization for building retrofit strategies: A model and an application (2012) Ener. Build, 44, pp. 81-87; Basuroy, S., Chuah, J.W., Jha, N.K., Making buildings energy-efficient through retrofits: A survey of available technologies (2013) Proc. IEEE Power and Energy Society Gen. Meet., pp. 1-5. , 21-25 July; Caccavelli, D., Gugerli, H., TOBUS- A European diagnosis and decision-making tool for office building upgrading (2002) Ener. Build, 34 (2), pp. 113-119; Carli, R., Albino, V., Dotoli, M., Mummolo, G., Savino, M., A dashboard and decision support tool for the energy governance of smart cities (2015) Proc. IEEE EESMS2015, 6. , July 9-10; Carli, R., Deidda, P., Dotoli, M., Pellegrino, R., An urban control center for the energy governance of a smart city (2014) Proc. IEEE ETFA2014, 6. , September 16-19; Carli, R., Dotoli, M., Pellegrino, R., Ranieri, L., Measuring and managing the smartness of cities: A framework for classifying performance indicators (2013) Proc. IEEE Conf. Systems, Man and Cybernetics (SMC 2013), pp. 1288-1293. , 13-16 Oct; Chuah, J.W., Raghunathan, A., Jha, N.K., ROBESim: A retrofitoriented building energy simulator based on EnergyPlus (2013) Energy and Buildings, 66; Dall'O, G., (2013) Green Energy Audit of Buildings-A Guide for A Sustainable Energy Audit of Buildings, , Springer: London, UK; Diakaki, C., Grigoroudis, E., Kabelis, N., Kolokotsa, D., Kalaitzakis, K., Stavrakakis, G., A multi-objective decision model for the improvement of energy efficiency in buildings (2010) Ener, 35 (12), pp. 5483-5496; Calcolo Semplificato Del Risparmio Annuo di Energia in Fonte Primaria Previsto Con un Intervento di Efficienza Energetica, , http://www.acs.enea.it/tecnici/calcolo_re.pdf, ENEA (in Italian); Directive 2002/91/ec of the european parliament and of the council on the energy performance of buildings (2003) L1/65, Off. J. Europ. Comm., , European Community; Towards a sustainable energy future (2008) IEA Programme of Work on Climate Change, Clean Energy and Sustainable Development, , http://ccs101.ca/assets/Documents/g8_towards_sustainable_future.pdf, International Energy Agency; Kaklauskas, A., Kazimieras Zavadskas, E., Raslanas, S., Multivariant design and multiple criteria analysis of building refurbishments (2005) Ener. Build, 37, pp. 361-372; Kolokotsa, D., Diakaki, C., Grigoroudis, E., Stavrakakis, G., Kalaitzakis, K., Decision support methodologies on the energy efficiency and energy management in buildings (2009) Adv. Build. Ener. Res, 3 (1), pp. 121-146; Liu, H., Zhao, Q.C., Huang, N.J., Zhao, X., A simulation-based tool for energy efficient building design for a class of manufacturing plants (2013) IEEE Trans. Aut. Sci. Eng, 10 (1), pp. 117-123; Ma, Z.J., Cooper, P., Daly, D., Ledo, L., Existing building retrofits: Methodology and state-of-the-art (2012) Ener. Build, 55, pp. 889-902; Marler, R.T., Arora, J.S., Survey of multi-objective optimization methods for engineering (2004) Structur. Multidisc. Optim, 26 (6), pp. 369-395; Pérez-Lombard, L., Ortiz, J., Pout, C., A review on buildings energy consumption information (2008) Ener. Build, 40, pp. 394-398; Roulet, C.A., Flourentzou, F., Labben, H.H., Santamouris, M., Koronaki, I., Dascalaki, E., Richalet, V., ORME: A multi-criteria rating methodology for buildings (2002) Build. Envir., 37 (6), pp. 579-586; Rysanek, A.M., Choudhary, R., Optimum building energy retrofits under technical and economic uncertainty (2013) Ener. Build, 57, pp. 324-337; Wulfinghoff, D.R., (1999) Energy Efficiency Manual, , Energy Institute Press, Wheaton, Maryland, US}, document_type={Conference Paper}, source={Scopus}, }`

- Carli, R., Dotoli, M., Epicoco, N., Angelico, B. & Vinciullo, A. (2015) Automated evaluation of urban traffic congestion using bus as a probe IN IEEE International Conference on Automation Science and Engineering., 967-972.

[Bibtex]`@CONFERENCE{Carli2015967, author={Carli, R. and Dotoli, M. and Epicoco, N. and Angelico, B. and Vinciullo, A.}, title={Automated evaluation of urban traffic congestion using bus as a probe}, journal={IEEE International Conference on Automation Science and Engineering}, year={2015}, volume={2015-October}, pages={967-972}, doi={10.1109/CoASE.2015.7294224}, art_number={7294224}, note={cited By 22}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84952762623&doi=10.1109%2fCoASE.2015.7294224&partnerID=40&md5=c2588eba274a644410a09da25d3699b5}, abstract={This paper presents an algorithm for the automated analysis and evaluation of vehicular traffic congestion in urban areas. The proposed approach is based on the concept of bus as a probe and makes use of GPS-generated data provided by a local transit bus tracking system. Archived GPS pulses are analyzed offline to extract valuable indices related to general urban traffic characteristics and aimed at generating a detailed view of the urban roads congestion. This information is useful both for policy makers, to effectively address the management of sustainable mobility in urban areas, and for citizens, to acquire awareness about congestion times and location zones. The presented algorithm is applied to a part of the urban road network of the municipality of Bari (Italy). © 2015 IEEE.}, keywords={Algorithms; Automation; Buses; Motor transportation; Probes, Automated analysis; Automated evaluation; Policy makers; Sustainable mobility; Tracking system; Urban road networks; Urban traffic; Urban traffic congestion, Traffic congestion}, references={Bacon, J., Bejan, A.I., Beresford, A.R., Evans, D., Gibbens, R.J., Moody, K., Using real-time road traffic data to evaluate congestion (2011) Lect. Notes Comp. Sc., 6875, pp. 93-117; Bertini, R.L., You are the traffic jam: An examination of congestion measures (2006) Transp. Res. Board 85th Ann. Meet., , Washington DC; Bertini, R.L., Tantiyanugulchai, S., Transit buses as traffic probes: Use of geolocation data for empirical evaluation (2004) Transp. Res. Rec., 1870 (1), pp. 35-45; Carli, R., Deidda, P., Dotoli, M., Pellegrino, R., An urban control center for the energy governance of a smart city (2014) Proc. 19th IEEE Int. Conf. Emerging Technologies and Factory Automation, , Barcelona, Spain, September 16-19; Carli, R., Dotoli, M., Pellegrino, R., Ranieri, L., Measuring and managing the smartness of cities: A framework for classifying performance indicators (2013) Proc. IEEE Int. Conf. Systems, Man and Cybernetics, pp. 1288-1293. , October 13-16; Carli, R., Dotoli, M., Pellegrino, R., Ranieri, L., A decision making technique to optimize a building stock energy efficiency (2015) IEEE Trans. Syst., Man, Cybern, Syst.; Casas, J., Torday, A., Perarnau, J., Breen, M., De Ruiz, V.A., Decision Support Systems (DSS) for traffic management assessment: Notes on current methodology and future requirements for the implementation of a DSS (2014) Proc. 5th Conf. Transp. Res. Arena: Transport Solutions from Research to Deployment, , Paris, France, April 14-17; Chabrol, M., Sarramia, D., Tchernev, N., Urban traffic systems modelling methodology (2006) Int. J. Prod. Econ., 99, pp. 156-176; Chakroborty, P., Kikuchi, S., Using bus travel time data to estimate travel times on urban corridors (2004) Transp. Res. Rec., 1870 (1), pp. 18-25; Chen, Y., Gao, L., Li, Z.-P., Liu, Y.-C., A new method for urban traffic state estimation based on vehicle tracking algorithm (2007) Proc. 2007 IEEE Intell. Transp. Sys. Conf., pp. 1097-1101. , Seattle, WA, USA, Sept. 30-Oct. 3; Chu, K.-C., Saitou, K., Optimization of probe vehicle deployment for traffic status estimation (2013) 2013 IEEE Int. Conf. on Automation Science and Engineering, pp. 880-885. , 17-20 Aug; Coifman, B., Kim, S., Using transit vehicles to measure freeway traffic conditions (2006) Proc. 9th Int. Conf. Applications of Advanced Technology in Transportation, , Chicago, Illinois, August, 13-16; Dotoli, M., Fanti, M.P., Iacobellis, G., An urban traffic network model by first order hybrid petri nets (2008) Proc. IEEE Int. Conf. Systems, Man and Cybernetics, pp. 1929-1934. , Singapore, 12-15 October; Dotoli, M., Fanti, M.P., Meloni, C., A signal timing plan formulation for urban traffic control (2006) Contr. Eng Pract, 14 (11), pp. 1297-1311. , November; Dotoli, M., Fanti, M.P., An urban traffic network model via coloured timed petri nets (2006) Contr. Eng Pract, 14 (10), pp. 1213-1229. , October; Dotoli, M., Hammadi, S., Jeribi, K., Russo, C., Zgaya, H., A multi-agent decision support system for optimization of co-modal transportation route planning services (2013) 52nd IEEE Conf. on Dec. and Contr., , Florence, Italy, December 10-13; Directive 2010/40/eu of the european parliament and of the council of 7 July 2010 on the framework for the deployment of intelligent transport systems in the field of road transport and for interfaces with other modes of transport (2010) Off. J. of the European Union, L 207, 53. , European Union, 6 August; Leduc, G., Road traffic data: Collection methods and applications (2008) Institute for Prospective Technological Studies, Joint Research Centre European Commission, p. 55; Litman, T., Developing indicators for comprehensive and sustainable transport planning (2007) Transp. Res. Rec., 2017 (1), pp. 10-15; Marchal, F., Hackney, J., Axhausen, K.W., Efficient map matching of large global positioning system data sets: Tests on speed-monitoring experiment in Zürich (2005) Transp. Res. Rec., 1935 (1), pp. 93-100; Pattara-Atikom, W., Pongpaibool, P., Thajchayapong, S., Estimating road traffic congestion using vehicle velocity (2006) Proc. 6th IEEE Int. Conf. ITS Telecom., pp. 1001-1004; Pu, W., Lin, J., Long, L., Real-time estimation of urban street segment travel time using buses as speed probes (2009) Transp. Res. Rec., 2129 (1), pp. 81-89; Taylor, M.A.P., Woolley, J.E., Zito, R., Integration of the global positioning system and geographical information systems for traffic congestion studies (2000) Transp. Res. C-Emer., 8 (1), pp. 257-285; (2000) Transportation Research Board, National Research Council, , Highway capacity manual. Washington DC; Uno, N., Kurauchi, F., Tamura, H., Iida, Y., Using bus probe data for analysis of travel time variability (2009) J. Intell. Transp. S., 13 (1), pp. 2-15; Zhu, T., Ma, F., Ma, T., Li, C., The prediction of bus arrival time using Global Positioning System data and dynamic traffic information (2011) Proc. IEEE Wireless and Mobile Networking Conf., pp. 1-5. , 2011 4th Joint IFIP}, document_type={Conference Paper}, source={Scopus}, }`

- Carli, R., Albino, V., Dotoli, M., Mummolo, G. & Savino, M. (2015) A dashboard and decision support tool for the energy governance of smart cities IN 2015 IEEE Workshop on Environmental, Energy, and Structural Monitoring Systems, EESMS 2015 – Proceedings., 23-28.

[Bibtex]`@CONFERENCE{Carli201523, author={Carli, R. and Albino, V. and Dotoli, M. and Mummolo, G. and Savino, M.}, title={A dashboard and decision support tool for the energy governance of smart cities}, journal={2015 IEEE Workshop on Environmental, Energy, and Structural Monitoring Systems, EESMS 2015 - Proceedings}, year={2015}, pages={23-28}, doi={10.1109/EESMS.2015.7175846}, art_number={7175846}, note={cited By 9}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84950984321&doi=10.1109%2fEESMS.2015.7175846&partnerID=40&md5=675d5e25da8794e638be9ff52797dd47}, abstract={The paper addresses the findings of the research activities conducted in the framework of the RES NOVAE project for the design and development of the Urban Control Center (UCC), a control room of the smart city that allows the Public Administration to analyze the city dynamics and citizens to receive information on the performance of urban infrastructure and services. With a specific focus on energy efficiency and environmental sustainability, we present the architecture of an innovative dashboard and decision support tool for efficient urban governance. We investigate solutions to effectively measure the city energy performance and proficiently support the decision maker in determining the optimal action plan for implementing smartness strategies in the city energy governance. © 2015 IEEE.}, author_keywords={decision support system; energy efficiency; indicators dashboard; information and communication technologies; management; monitoring; multi-attribute analysis; multi-objective optimization; optimization; smart cities}, keywords={Artificial intelligence; Decision making; Decision support systems; Information management; Management; Monitoring; Multiobjective optimization; Optimization; Public administration; Sustainable development, Decision support tools; Design and Development; Environmental sustainability; Information and Communication Technologies; Multi-attribute analysis; Research activities; Smart cities; Urban infrastructure, Energy efficiency}, references={Albino, V., Berardi, U., Dangelico, R.M., Smart cities: Definitions, dimensions, performance, and initiatives (2015) Journal of Urban Technology, (21); Batty, M., Axhausen, K.W., Smart cities of the future (2012) Eur. Phys. J., 214 (1), pp. 481-518; Bhowmick, A., Francellino, E., (2012) IBM Intelligent Operations Center for Smarter Cities Administration Guide IBM Redbooks; Caponio, G., D'Alessandro, G., Digiesi, S., Mossa, G., Mummolo, G., Verriello, R., Minimizing carbon-footprint of municipal waste separate collection systems (2015) Enhancing Synergies in A Collaborative Environment, pp. 351-359. , Springer International Publishing; Caragliu, A., Del Bo, C., Nijkamp, P., Smart cities in Europe (2009) Proc. 3rd Centr. Europ. Conf. Regional Science, , Oct; Carli, R., Deidda, P., Dotoli, M., Pellegrino, R., An urban control center for the energy governance of a smart city (2014) 19th IEEE International Conference on Emerging Technologies and Factory Automation, , Barcelona, Spain, September 16-19; Carli, R., Dotoli, M., Pellegrino, R., Ranieri, L., Measuring and managing the smartness of cities: A framework for classifying performance indicators (2013) Proc. IEEE Conf. Systems, Man and Cybernetics, pp. 1288-1293. , 13-16 Oct; Carli, R., Dotoli, M., Pellegrino, R., ICT and optimization for the energy management of smart cities: The street lighting decision panel (2015) IEEE ETFA2015; Chourabi, H., Nam, T., Understanding smart cities: An integrative framework (2012) Proc. System Science (HICSS), 2012 45th Hawaii Int. Conf., pp. 2289-2297. , 4-7 Jan; Eagle, N., Pentland, A., Reality mining: Sensing complex social systems (2006) Personal Ubiquitous Computing, 10, pp. 255-268; EU Commission (2012) Communication from the Commission to the European Parliament, The Council, The European Economic and Social Committee and the Committee of the Regions, 'A European strategy for key enabling technologies-A bridge to growth and jobs'; Giffinger, R., Fertner, C., (2007) Smart Cities: Ranking of European Medium-Sized Cities, , http://www.smartcities.eu/download/smart_cities_final_report.pdf, Vienna, Austria: Centre of Regional Science (SRF), Vienna University of Technology; Harrison, C., Eckman, B., Foundations for smarter cities (2010) IBM Journal of Research and Development, 54 (4), pp. 1-16; Mitton, N., Papavassiliou, S., Puliafito, A., Trivedi, K.S., Combining Cloud and sensors in a smart city environment (2012) EURASIP Journal on Wireless Communications and Networking 2012, p. 247; Naphade, M., Banavar, Smarter cities and their innovation challenges (2011) Computer, 44 (6), pp. 32-39. , June; Savino, T., Albino, V., Dangelico, R.M., (2014) Environmental Management to Improve Quality of Life in Smart Cities, , IFKAD; Vicente, L.N., Calamai, P.H., Bilevel and multilevel programming: A bibliography review (1994) J. Glob. Optim., 5 (3), pp. 291-306; Washburn, D., Sindhu, (2010) Helping CIOs Understand "smart City" Initiatives, , Cambridge, MA: Forrester Research}, document_type={Conference Paper}, source={Scopus}, }`

- Dotoli, M., Epicoco, N., Falagario, M., Costantino, N. & Turchiano, B. (2015) An integrated approach for warehouse analysis and optimization: A case study. IN Computers in Industry, 70.56-69.

[Bibtex]`@ARTICLE{Dotoli201556, author={Dotoli, M. and Epicoco, N. and Falagario, M. and Costantino, N. and Turchiano, B.}, title={An integrated approach for warehouse analysis and optimization: A case study}, journal={Computers in Industry}, year={2015}, volume={70}, number={1}, pages={56-69}, doi={10.1016/j.compind.2014.12.004}, note={cited By 42}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84926355397&doi=10.1016%2fj.compind.2014.12.004&partnerID=40&md5=a12e368cf5ec2e7108d105646b343320}, abstract={The paper focuses on the analysis and optimization of production warehouses, proposing a novel approach to reduce inefficiencies which employs three lean manufacturing tools in an integrated and iterative framework. The proposed approach integrates the Unified Modeling Language (UML) - providing a detailed description of the warehouse logistics - the Value Stream Mapping (VSM) tool - identifying non-value adding activities - and a mathematical formulation of the so-called Genba Shikumi philosophy - ranking such system anomalies and assessing how they affect the warehouse. The subsequent reapplication of the VSM produces a complete picture of the reengineered warehouse, and using the UML tool allows describing in detail the updated system. By applying the presented methodology to the warehouse of an Italian interior design producer, we show that it represents a useful tool to systematically and dynamically improve the warehouse management. Indeed, the application of the approach to the company leads to an innovative proposal for the warehouse analysis and optimization: a warehouse management system that leads to increased profitability and quality as well as to reduced errors. © 2014 Elsevier B.V. All rights reserved.}, author_keywords={Analysis; Genba Shikumi; Optimization; Unified modeling language; Value stream mapping; Warehouse}, keywords={Architectural design; Computer hardware description languages; Industrial management; Iterative methods; Mapping; Optimization; Quality control; Unified Modeling Language, Analysis; Genba Shikumi; Manufacturing tools; Mathematical formulation; Value adding activities; Value stream mapping; Warehouse management; Warehouse management systems, Warehouses}, references={Bevilacqua, V., Costantino, N., Dotoli, M., Falagario, M., Sciancalepore, F., Strategic design and multi-objective optimization of distribution networks based on genetic algorithms (2012) International Journal of Computer Integrated Manufacturing, 25 (12), pp. 1139-1150; Costantino, N., Dotoli, M., Falagario, M., Fanti, M.P., Mangini, A.M., Sciancalepore, F., (2011) A Fuzzy Programming Approach for the Strategic Design of Distribution Networks, , CASE 2011, Trieste Italy, 24-27.08.2011; Costantino, N., Dotoli, M., Falagario, M., Fanti, M.P., Mangini, A.M., A model for supply management of agile manufacturing supply chains (2012) International Journal of Production Economics, 135, pp. 451-457; Dotoli, M., Fanti, M.P., Meloni, C., Zhou, M.C., A multi-level approach for network design of integrated supply chains (2005) International Journal of Production Research, 43 (20), pp. 4267-4287; Dotoli, M., Fanti, M.P., Meloni, C., Zhou, M.C., Design and optimization of integrated e-supply chain for agile and environmentally conscious manufacturing (2006) IEEE Transactions on Systems Man and Cybernetics, Part A, 36 (1), pp. 62-75; Costantino, N., Dotoli, M., Falagario, M., Fanti, M.P., Mangini, A.M., Sciancalepore, F., Ukovich, W., A model for the strategic design of distribution networks (2010) CASE 2010, , Toronto Canada, 21-24.08.2010; Dassisti, M., Dotoli, M., Epicoco, N., Falagario, M., Internal logistics integration by automated storage and retrieval systems: A reengineering case study (2012) Industry Case Studies Program of the 7th International Workshop on Enterprise Integration, Interoperability and Networking, pp. 78-82. , Rome, Italy; De Koster, R., Le-Duc, T., Roodbergen, K.J., Design and control of warehouse order picking: A literature review (2007) European Journal of Operational Research, 182, pp. 481-501; Dotoli, M., Fanti, M.P., A coloured timed Petri net model for automated storage and retrieval systems serviced by rail-guided vehicles: A control perspective (2005) International Journal of Computer Integrated Manufacturing, 18, pp. 122-136; Dotoli, M., Fanti, M.P., Deadlock detection and avoidance strategies for automated storage and retrieval systems (2007) IEEE Transactions on Systems, Man and Cybernetics, Part C, 37, pp. 541-552; Rouwenhorst, B., Reuterb, B., Stockrahmb, V., Van Houtumc, G.J., Mantela, R.J., Zijmc, W.H.M., Warehouse design and control: Framework and literature review (2000) European Journal of Operational Research, 122, pp. 515-533; Costantino, N., Dotoli, M., Epicoco, N., Falagario, M., Sciancalepore, F., (2012) A Novel Fuzzy Data Envelopment Analysis Methodology for Performance Evaluation in a Twostage Supply Chain, , CASE 2012, Seoul Korea, 20-24.08.2012; Van Den Berg, J.P., Zijm, W.H.M., Models for warehouse management: Classification and examples (1999) International Journal of Production Economics, 59, pp. 519-528; Van Den Berg, J.P., A literature survey on planning and control of warehousing systems (1999) IIE Transactions, 31, pp. 751-762; Johnson, A., McGinnis, L., Performance measurement in the warehousing industry (2011) IIE Transactions, 43 (3), pp. 220-230; Gu, J.X., Goetschalckx, M., McGinnis, L.F., Research on warehouse design and performance evaluation: A comprehensive review (2010) European Journal of Operational Research, 203 (3), pp. 539-549; Baker, P., Canessa, M., Warehouse design: A structured approach (2009) European Journal of Operational Research, 193 (2), pp. 425-436; Gagliardi, J.P., Renaud, J., Ruiz, A., A simulation model to improve warehouse operations (2007) Proc. Winter Simulation Conference, pp. 2012-2018. , 9-12 December 2007, Univ. Laval, Quebec, Canada; Karlsson, C., Áhlström, P., A lean and global smaller firm? (1997) International Journal of Operations & Production Management, 17 (10), pp. 940-952; Narasimhan, J., Parthasarathy, L., Narayan, P.S., Inncreasing the effectiveness of value stream mapping using simulation tools in engine test operations (2007) 18th IASTED International Conference on Modeling and Simulation, pp. 260-264. , Montreal, Canada; Abdulmalek, A., Rajgopal, J., Analyzing the benefits of lean manufacturing and value stream mapping via simulation: A process sector case study (2007) International Journal of Production Economics, 107 (1), pp. 223-236; Ackerman, K., (2007) Lean Warehousing, , Ackerman Publications; Bartholomew, D., (2008) Putting Lean Principles in the Warehouse, , Lean Enterprise Institute; Hines, P., Rich, N., The seven value stream mapping tools (1997) International Journal of Operations & Production Management, 17 (1), pp. 46-64; Keil, S., Schneider, G., Eberts, D., Kristina Wilhelm, I., Gestring, R., Lasch, A., Deeutschla Ünder, Establishing continuous flow manufacturing in a Wafertest-environment via value stream design (2011) 22nd IEEE/SEMI Advanced Semiconductor Manufacturing Conference, p. 7; McManus, H.L., Millard, R.L., Value stream analysis and mapping for product development (2002) 23rd Congress International Council of the Aeronautical Sciences, pp. 61031-610310; Ramesh, V., Sreenivasa Prasad, K.V., Srinivas, T.R., Implementation of a lean model for carrying out value stream mapping in a manufacturing industry (2008) Journal of Industrial and Systems Engineering, 2, pp. 180-196; Dharmapriya, U.S.S., Kulatunga, A.K., New strategy for warehouse optimization. Lean warehousing (2011) Proc. of the 2011 International Conference on Industrial Engineering and Operations Management, , Kuala Lumpur, Malaysia, January 22-24; Gopakumar, B., Sundaram, S., Wang, S.Y., Koli, S., Srihari, K., A simulation based approach for dock allocation in a food distribution center (2008) Proc. Winter Simulation Conference, WSC 2008, pp. 2750-2755. , 7-10 December 2008; Chen, J.C., Cheng, C.-H., Huang, P.T.B., Wang, K.-J., Huang, C.-J., Ting, T.-C., Warehouse management with lean and RFID application: A case study (2013) International Journal of Advanced Manufacturing Technology, p. 12. , Published online 18 May 2013; Seth, D., Gupta, V., Application of value stream mapping for lean operations and cycle time reduction: An Indian case study (2005) Production Planning & Control: The Management of Operations, 16 (1), pp. 44-59; Scott, N.A., Lean conversion and Genba Shikumi (2007) International Conference on Agile Manufacturing, pp. 168-171; Subramaniam, S., Husin, S., Yusop, Y., Hamidon, A., Real time shop floor monitoring system for a better production line management (2007) Proc. Asia-pacific Conference on Applied Electromagnetics, 2007, pp. 1-4. , Melaka, Malaysia, 4-6 December 2007; Jorapur, V.S., Puranik, V.S., Deshpande, A.S., Research issues in detection of bottlenecks in discrete manufacturing systems - A review (2012) International Journal of Engineering Research & Technology, 1 (8), p. 6; Imai, M., (1986) Kaizen: The Key to Japan's Competitive Success, , McGraw-Hill; Palmer, V.S., Inventory management Kaizen (2001) Proc. 2nd Int. Workshop on Engineering Management for Applied Technology, pp. 55-56. , Austin, Texas, 16-17 August 2001; Audenino, A., Kaizen and lean management autonomy and self-orientation, potentiality and reality (2012) Proc. 2nd International Conference on Communications, Computing and Control Applications, pp. 1-6. , Marseille, France, 6-8 December 2012; Moen, R., Norman, C., (2009) Evolution of the PDCA Cycle, pp. 5-9. , API Publications; Shingo, S., Dillon, A., (1989) A Study of the Toyota Production System from an Industrial Engineering Viewpoint, , Productivity Press, Portland, OR; Rooney, J.J., Heuvel, L.N.V., Root cause analysis for beginners (2004) Quality Progress, 37 (7), pp. 45-53; Greif, M., (1991) The Visual Factory: Building Participation Through Shared Information, , Productivity Press, Portland, OR; Poppendieck, M., Poppendieck, T., (2003) Lean Software Development: An Agile Toolkit, , Addison-Wesley Professional; Shah, R., Ward, P.T., Lean manufacturing: Context, practice bundles, and performance (2003) Journal of Operations Management, 21, pp. 129-149; Mo, J.P.T., The role of lean in the application of information technology to manufacturing (2009) Computers in Industry, 60 (4), pp. 266-276; Riezebos, J., Klingenberg, W., The role of IT in advancing lean manufacturing (2009) Computers in Industry, 60 (4), pp. 235-236; Riezebos, J., Klingenberg, W., Hicks, C., Lean production and information technology: Connection or contradiction? (2009) Computers in Industry, 60 (4), pp. 237-247; Powell, D., Alfnes, E., Strandhagen, J.O., Dreyer, H., The concurrent application of lean production and ERP: Towards an ERP-based lean implementation process (2013) Computers in Industry, 64 (3), pp. 324-335; Christopher, M., (2010) Logistics and Supply Chain Management: Creating Value-adding Networks, , 4th ed., Prentice Hall, Edinburgh, UK; Kofjac, D., Kljajic, M., Rejec, V., The anticipative concept in warehouse optimization using simulation in an uncertain environment (2009) European Journal of Operational Research, 193 (3), pp. 660-669; Booch, G., Jacobson, I., Rumbaugh, J., (2005) The Unified Modeling Language User Guide, , 2nd ed., Addison Wesley Longman, Computer & Engineering Publishing Group; Womack, J.P., Jones, D.T., (1996) Lean Thinking, , Simon and Schuster, New York; Holt, J., (2004) UML for Systems Engineering: Watching the Wheels, , 2nd ed., The Institution of Engineering and Technology; Rother, M., Shook, J., (1999) Learning to See: Value Stream Mapping to Add Value and Eliminate Muda, , The Lean Enterprise Institute, Inc., Brookline, MA; Pan, G.-Q., Feng, D.-Z., Jiang, M.-X., Application research of shortening delivery time through value stream mapping analysis (2010) IEEE 17th International Conference on Industrial Engineering and Engineering Management, pp. 733-736; Braglia, M., Carmignani, G., Zammori, F., A new value stream mapping approach for complex production system (2006) International Journal of Production Research, 44, pp. 3929-3952; Shiau, J.-Y., Lee, M.C., A warehouse management system with sequential picking for multi-container deliveries (2010) Computers & Industrial Engineering, 58, pp. 382-392}, document_type={Article}, source={Scopus}, }`

- Dotoli, M., Fay, A., Miskowicz, M. & Seatzu, C. (2015) A survey on advanced control approaches in factory automation IN IFAC-PapersOnLine., 394-399.

[Bibtex]`@CONFERENCE{Dotoli2015394, author={Dotoli, M. and Fay, A. and Miskowicz, M. and Seatzu, C.}, title={A survey on advanced control approaches in factory automation}, journal={IFAC-PapersOnLine}, year={2015}, volume={28}, number={3}, pages={394-399}, doi={10.1016/j.ifacol.2015.06.113}, note={cited By 6}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84953865196&doi=10.1016%2fj.ifacol.2015.06.113&partnerID=40&md5=7487e152c395d18b98f21b4ebcbe27f2}, abstract={The goal of this paper consists in providing a survey of the main advanced control techniques currently adopted in factory automation. In particular, attention is devoted to model based control, model predictive control, intelligent and adaptive control, discrete event and event-triggered control. Open issues and challenges are pointed out, and the needs for further research efforts are discussed in detail. © 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.}, author_keywords={Adaptive control; Advanced control; Discrete event control; Event-triggered control; Factory automation; Intelligent control; Model based control; Model predictive control}, keywords={Discrete event simulation; Factory automation; Intelligent control; Surveys; Adaptive control systems; Automation; Model predictive control, Adaptive Control; Advanced control; Discrete event control; Event-triggered controls; Model based controls, Model predictive control; Discrete event simulation}, references={Anta, A., Tabuada, P., To sample or not to sample: Self-triggered control for nonlinear systems (2010) IEEE Transactions on Automatic Control, 55 (9), pp. 2030-2042; Åstrom, K.J., Kumar, P.R., Control: A perspective (2014) Automatica, 50 (1), pp. 3-43. , Jan; Bemporad, A., Morari, M., Robust model predictive control: A survey (1999) Robustness in Identification and Control, 245, pp. 207-226. , A. Garulli, A. Tesi, and A. Vicino, eds., Lecture Notes in Control and Information Sciences Springer-Verlag; Camacho, E.F., Alba Bordons, C., (2007) Model Predictive Control, , Springer-Verlag London, UK; Camacho, E.F., Ramirez, D.R., Limon, D., Munoz De La Pena, D., Alamo, T., Model predictive control techniques for hybrid systems (2010) Annual Reviews in Control, 34 (1), pp. 21-31. , Apr; Cassandras, C., Lafortune, S., (2008) Introduction to Discrete Event Systems, , (2nd Ed.), Kluwer Academic Publishers, Boston, MA; Cerman, O., Fuzzy model reference control with adaptation mechanism (2013) Expert Systems with Applications, 40 (13), pp. 5181-5187; Christofides, P.D., Scattolini, R., Munoz De La Pena, D., Liu, J., Distributed model predictive control: A tutorial review and future research directions (2013) Computers & Chemical Engineering, 51, pp. 21-41. , Apr; Darby, M.L., Harmse, M., Nikolaou, M., MPC: Current practice and challenges (2012) Control Engineering Practice, 20 (4). , Apr; Dereli, T., Baykasoglu, A., Altun, K., Durmusoglu, A., Türksen, I.B., Industrial applications of type-2 fuzzy sets and systems: A concise review (2011) Computers in Industry, 62 (2), pp. 125-137. , Feb; Feng, G., A survey on analysis and design of model-based fuzzy control systems (2006) Fuzzy Systems, IEEE Trans, On, 14 (5), p. 676. , 697, Oct; Fleming, P.J., Purshouse, R.C., Evolutionary algorithms in control systems engineering: A survey (2002) Control Engineering Practice, 10 (11), pp. 1223-1241. , Nov; Grune, L., Hirche, S., Junge, O., Koltai, P., Lehmann, D., Lunze, J., Event-based control (2014) Control Theory of Digitally Networked Dynamic Systems, pp. 169-261. , Ed. J. Lunze Springer; Heemels, W.P.M.H., Donkers, M.C.F., Model-based periodic event-triggered control for linear systems (2013) Automatica, 49, pp. 698-711; Heemels, W.P.M.H., Johansson, K.H., Tabuada, P., An introduction to event-triggered and self-triggered control (2012) Proc. IEEE Conference on Decision and Control, pp. 3270-3285; Huang, Y., Advances in artificial neural networks - Methodological development and application (2009) Algorithms, 2 (3), pp. 973-1007; Jämsä-Jounela, S.-L., Future trends in process automation (2007) Annual Reviews in Control, 31 (2), pp. 211-220; Lafortune, S., On decentralized and distributed control of partially-observed discrete event systems (2007) Advances in Control Theory and Applications, Lecture Notes in Control and Information Sciences, 353, pp. 171-184. , Springer-Verlag, Berlin Heidelberg; Lee, J.H., Model predictive control: Review of the three decades of development (2011) International Journal of Control Automation and Systems, 9 (3), pp. 415-424; Leith, D.J., Leithead, W.E., Survey of gain-scheduling analysis and design (2000) Int. Journal of Control, 73 (11), pp. 1001-1025; Magali, R.G., Meireles, P.E.M., Simöes, M.G.S., A comprehensive review for industrial applicability of artificial neural networks (2003) IEEE Trans, on Industrial Electronics, 50 (3). , June; Mendes, J., Araüjo, R., Souza, F., Adaptive fuzzy identification and predictive control for industrial processes (2013) Expert Systems with Applications, 40 (17), pp. 6964-6975; Miskowicz, M., Event-based sampling strategies in networked control systems (2014) Proc. IEEE Workshop on Factory Communication Systems, pp. 1-10; Morari, M., Predicting the future of model predictive control (2009) Workshop in Celebration of David Clarke's Contribution to MPC, , Univ. of Oxford, Jan; Morel, G., Valckenaers, P., Faure, J.-M., Pereira, C.E., Diedrich, C., Manufacturing plant control challenges and issues (2007) Control Engineering Practice, 15 (11), pp. 1321-1331. , Nov; Nowzari, C., Cortés, J., Team-triggered coordination of networked systems (2013) Proc. American Control Conference, pp. 3821-3826; Pathak, K.B., Adhyaru, D.M., Survey of model reference adaptive control (2012) 2012 NIRMA Univ. Int. Conf. on Eng., , Ahmedabad, India, Dec; Pawlowski, A., Cervin, A., Guzmán, J.L., Berenguel, M., Generalized predictive control with actuator deadband for event-based approaches (2014) IEEE Trans, on Industrial Informatics, 10 (1), pp. 523-537; Ploennigs, J., Vasyutynskyy, V., Kabitzsch, K., Comparative study of energy-efficient sampling approaches for wireless control networks (2010) IEEE Transactions on Industrial Informatics, 6 (3), pp. 416-424; Precup, R.-E., Hellendoorn, H., A survey on industrial applications of fuzzy control (2011) Computers in Industry, 62 (3), pp. 213-226. , Apr; Sánchez, J., Visioli, A., Dormido, S., Event-based PID control (2012) PID Control in the Third Millennium. Advances in Industrial Control, pp. 495-526. , Ed. R. Vilanova, A. Visioli Springer; Skogestad, S., Postlethwaite, I., (2005) Multivariable Feedback Control: Analysis and Design, , (2nd Ed.), Wiley; Sijs, J., Noack, B., Hanebeck, U.D., Event-based state estimation with negative information (2013) Proc. IEEE Int. Conf. on Information Fusion, pp. 2192-2199}, document_type={Conference Paper}, source={Scopus}, }`

- Carli, R. & Dotoli, M. (2015) A decentralized resource allocation approach for sharing renewable energy among interconnected smart homes IN Proceedings of the IEEE Conference on Decision and Control., 5903-5908.

[Bibtex]`@CONFERENCE{Carli20155903, author={Carli, R. and Dotoli, M.}, title={A decentralized resource allocation approach for sharing renewable energy among interconnected smart homes}, journal={Proceedings of the IEEE Conference on Decision and Control}, year={2015}, volume={54rd IEEE Conference on Decision and Control,CDC 2015}, pages={5903-5908}, doi={10.1109/CDC.2015.7403147}, art_number={7403147}, note={cited By 29}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84962024908&doi=10.1109%2fCDC.2015.7403147&partnerID=40&md5=194027b41fd3ace430f8f40254ace4de}, abstract={The paper deals with the scheduling of energy activities of a group of interconnected users that buy energy from a producer and share a renewable energy source. The scheduling problem is stated and solved with a twofold goal. First, the model is formulated to ensure social welfare-optimal allocation of the energy produced from the shared renewable energy generator. Second, the model aims at cost-optimal planning of users' controllable appliances taking into account a realistic time-varying quadratic pricing of the energy bought from the distribution network. The solution approach relies on a decentralized optimization algorithm that is composed by a two-level iterative procedure combining Gauss-Seidel decomposition with competitive game formulation. A case study simulated in different scenarios demonstrates that the approach allows exploiting the potential of renewable energy sources' sharing to reduce individual users' energy consumption costs, limiting the peak average ratio of energy profiles and complying with the customer's energy needs. © 2015 IEEE.}, author_keywords={Energy consumption; Games; Home appliances; Optimization; Pricing; Renewable energy sources; Resource management}, keywords={Algorithms; Automation; Costs; Domestic appliances; Economics; Energy utilization; Intelligent buildings; Iterative methods; Natural resources; Optimization; Resource allocation; Scheduling; Time varying networks, Decentralized optimization; Decentralized resource allocation; Games; Optimal allocation; Renewable energy generators; Renewable energy source; Resource management; Scheduling problem, Renewable energy resources}, references={Adika, C.O., Wang, L.F., Non-cooperative decentralized charging of homogeneous households' batteries in a smart grid (2014) IEEE Trans. Smart Grids, 5 (4), pp. 1855-1863; Agarwal, T., Cui, S., Noncooperative Games for Autonomous Consumer Load Balancing over Smart Grid, , http://arxiv.org/abs/1104.3802; Atzeni, I., Ordonez, L.G., Scutari, G., Palomar, D.P., Fonollosa, J.R., Demand-side management via distributed energy generation and storage optimization (2013) IEEE Trans. Smart Grids, 4 (2), pp. 866-876; Barbato, A., Capone, A., Carello, G., Delfanti, M., Merlo, M., Zaminga, A., House energy demand optimization in single and multiuser scenarios (2011) Proc. IEEE Int. Conf. Smart Grids Comm., pp. 345-350. , Oct. 17-20; Bertsekas, D.P., Tsitsiklis, J.N., (1989) Parallel and Distributed Computation: Numerical Methods, 23. , Englewood Cliffs, NJ: Prentice Hall; Boyd, S., Vandenberghe, L., (2004) Convex Optimization, , Cambridge University Press, UK; Carli, R., Deidda, P., Dotoli, M., Pellegrino, R., Ranieri, L., An urban control center for the energy governance of a smart city (2014) Proc. IEEE Int. Conf. Emerg. Techn. Fact. Autom., , Sept. 16-19; Carli, R., Dotoli, M., Pellegrino, R., ICT and optimization for the energy management of smart cities: The street lighting decision panel (2015) Proc. IEEE Int. Conf. Emerg. Techn. Fact. Autom., , Sept. 8-11; Carli, R., Dotoli, M., Energy scheduling of a smart home under nonlinear pricing (2014) Proc. IEEE Int. Conf. Dec. Contr., pp. 5648-5653. , Dec. 15-17; Cavraro, G., Carli, R., Zampieri, S., A distributed control algorithm for the minimization of the power generation cost in smart micro-grid (2014) Proc. IEEE Int. Conf. Dec. Contr., pp. 5642-5647. , Dec. 15-17; Deng, R., Yang, Z., Chen, J., Asr, N.R., Chow, M.Y., Residential energy consumption scheduling: A coupled-constraint game approach (2014) IEEE Trans. Smart Grids, 5 (3), pp. 1340-1350. , May; Huang, Z., Zhu, T., Gu, Y., Irwin, D., Mishra, A., Shenoy, P., Minimizing electricity costs by sharing energy in sustainable microgrids Proc. ACM Conf. Embed. Sys. Ener.-Effic. Build., pp. 120-129. , Nov. 2014; Johari, R., Tsitsiklis, J.N., A game theoretic view of efficiency loss in resource allocation (2005) Advances in Control, Communication Networks, and Transportation Systems, pp. 203-223. , Birkhäuser, Boston; Katoh, N., Shioura, A., Ibaraki T, T., Resource allocation problems (2013) Handbook of Combinatorial Optimization, pp. 2897-2988; Kelly, F., Charging and rate control for elastic traffic (1997) Eur. Trans. Telecom., 8 (1), pp. 33-37; Mohsenian-Rad, A.-H., Leon-Garcia, A., Optimal residential load control with price prediction in real-time electricity pricing environments (2010) IEEE Trans. Smart Grids, 1, pp. 120-133; Parise, F., Colombino, M., Grammatico, S., Lygeros, J., Mean field constrained charging policy for large populations of Plug-in Electric Vehicles (2014) Proc. IEEE Int. Conf. Dec. Contr., pp. 5101-5106. , Dec. 15-17, Dec; Rosen, J.B., Existence and uniqueness of equilibrium points for concave n-person games (1965) Econometr., 33, pp. 347-351; Safdarian, A., Fotuhi-Firuzabad, M., Lehtonen, M., A distributed algorithm for managing residential demand response in smart grids (2014) IEEE Trans. Ind. Inf., 10 (4), pp. 2385-2393. , Nov; Sun, B., Luh, P.B., Jia, Q.-S., Jiang, Z., Wang, F., Song, C., Building energy management: Integrated control of active and passive heating, cooling, lighting, shading, and ventilation systems (2013) IEEE Trans. Aut. Sci. Eng., 10 (3), pp. 588-602; Zhu, T., Xiao, S., Ping, Y., Towsley, D., Gong, W., A secure energy routing mechanism for sharing renewable energy in smart microgrid (2011) Proc. IEEE Int. Conf. Smart Grids Comm., pp. 143-148. , Oct. 17-20; Zhu, T., Huang, Z., Sharma, A., Su, J., Irwin, D., Mishra, A., Shenoy, P., Sharing renewable energy in smart microgrids (2013) Proc. ACM/IEEE Int. Conf. Cyber-Phys. Sys., pp. 219-228. , April}, document_type={Conference Paper}, source={Scopus}, }`

- Dotoli, M., Epicoco, N. & Falagario, M. (2015) A Technique for Supply Chain Network Design under Uncertainty using Cross-Efficiency Fuzzy Data Envelopment Analysis IN IFAC-PapersOnLine., 634-639.

[Bibtex]`@CONFERENCE{Dotoli2015634, author={Dotoli, M. and Epicoco, N. and Falagario, M.}, title={A Technique for Supply Chain Network Design under Uncertainty using Cross-Efficiency Fuzzy Data Envelopment Analysis}, journal={IFAC-PapersOnLine}, year={2015}, volume={48}, number={3}, pages={634-639}, doi={10.1016/j.ifacol.2015.06.153}, note={cited By 8}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84953877988&doi=10.1016%2fj.ifacol.2015.06.153&partnerID=40&md5=80301c5e693bc3b2946766f9b7165674}, abstract={The paper focuses on Supply Chain Network Design (SCND) under uncertainty. We propose a SCND method extending an approach originally proposed by some of the authors for supplier ranking. The novel method integrates the cross-efficiency Data Envelopment Analysis (DEA) and fuzzy set theory to manage the SCND problem considering nondeterministic input and output data. After ranking all the actors belonging to each SCN stage, a linear integer programming model is stated and solved for each pair of subsequent SC stages to maximize the overall SCN efficiency, while respecting the available capacity at each node and satisfying customers' demand. A case study is presented to show the technique effectiveness. © 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.}, author_keywords={cross-efficiency; data envelopment analysis; fuzzy logic; supply chain network design; uncertainty}, keywords={Data envelopment analysis; Efficiency; Fuzzy set theory; Integer programming; Supply chains; Uncertainty analysis, Available capacity; Cross efficiency; Fuzzy data envelopment analysis; Input and outputs; Linear integer programming; Supply chain network design; uncertainty, Fuzzy logic}, references={Amirteimoori, A., Kordrostami, S., Production planning in Data Envelopment Analysis (2012) Int J Prod Econ, 140, pp. 212-218; Charnes, A., Cooper, W.W., Rhodes, E., Measuring the efficiency of decision making units (1978) Eur J Oper Res, 2, pp. 429-444; Costantino, N., Dotoli, M., Falagario, M., Fanti, M.P., Mangini, A.M., Sciancalepore, F., Ukovich, W., A model for the strategic design of Distribution Networks (2010) Proc. IEEE Conf. on Automation Science and Engineering (CASE), pp. 21-24. , Toronto (Canada) August 2010; Costantino, N., Dotoli, M., Falagario, M., Fanti, M.P., Mangini, A.M., Sciancalepore, F., Supplier selection in the public procurement sector via a Data Envelopment Analysis approach (2011) Proc. 19th IEEE Mediterranean Conf. on Control and Automation (MED 2011), , Corfu, Greece June 23-25, 2011; Costantino, N., Dotoli, M., Falagario, M., Fanti, M.P., Mangini, A.M., Sciancalepore, F., Ukovich, W., A fuzzy programming approach for the strategic design of distribution networks (2011) Proc. 2011 IEEE Conf. on Automation Science and Engineering (CASE), , Trieste, Italy August 24-27, 2011; Costantino, N., Dotoli, M., Falagario, M., Fanti, M.P., Mangini, A.M., A model for supply management of agile manufacturing Supply Chains (2012) Int J Prod Econ, 135 (1), pp. 451-457; Costantino, N., Dotoli, M., Falagario, M., Sciancalepore, F., Fuzzy network design of sustainable Supply Chains (2012) Information Control Problems in Manufacturing, 14 (1), pp. 1284-1289; Dotoli, M., Epicoco, N., Falagario, M., Sciancalepore, F., A cross efficiency fuzzy Data Envelopment Analysis technique for performance evaluation of decision making units under uncertainty (2014) Computers and Industrial Engineering, , hhtp://dx.doi.org/, to appear, doi; Farahani, R.Z., Rezapour, S., Drezner, T., Fallah, S., Competitive supply chain network design: An overview of classifications, models, solution techniques and applications (2014) Omega, 45, pp. 92-118; Kao, C., Efficiency decomposition in network data envelopment analysis: A relational model (2009) Eur J Oper Res, 192, pp. 949-962; Kao, C., Efficiency measurement for parallel production systems (2009) Eur J Oper Res, 196, pp. 1107-1112; Kao, C., Network Data Envelopment Analysis with Fuzzy Data (2014) Performance Measurement with Fuzzy Data Envelopment Analysis, Studies in Fuzziness and Soft Computing, 309, pp. 191-206. , A. Emrouznejad M. Tavana; Liang, L., Feng, Y., Wade, D.C., Joe, Z., DEA models for supply chain efficiency evaluation (2006) Annals of Operations Research, 145, pp. 35-49; Liang, T.-F., Application of fuzzy sets to manufacturing/distribution planning decisions in supply chains (2011) Information Science, 181, pp. 842-854; Liu, S.-T., Chuang, M., Fuzzy efficiency measures in fuzzy DEA/AR with application to university libraries (2009) Expert Systems with Applications, 36 (2), pp. 1105-1113; Lozano, S., Moreno, P., Network fuzzy Data Envelopment Analysis (2014) Performance Measurement with Fuzzy Data Envelopment Analysis, Studies in Fuzziness and Soft Computing, 309, pp. 207-230. , A. Emrouznejad M. Tavana; Matinrad, N., Roghaniana, E., Rasib, Z., Supply chain network optimization: A review of classification, models, solution techniques and future research (2013) Uncertain Supply Chain Management, 1, pp. 1-24; Mirzapour Al-e-Hashem, S.M.J., Maleklyand, H., Aryanezhad, M.B., A multi-objective robust optimization model for multi-product multi-site aggregate production planning in a supply chain under uncertainty (2011) Int J Prod Econ, 134, pp. 28-42; Melo, M.T., Nickel, S., Saldanha-Da-Gama, F., Facility location and supply chain management - A review (2009) Eur J Oper Res, 196, pp. 401-412; Peidro, D., Mula, J., Poler, R., Verdegay, J., Fuzzy optimization for supply chain planning under supply, demand and process uncertainties (2009) Fuzzy Sets and Systems, 160 (18), pp. 2640-2657; Pfohl, H.-C., Köhler, H., Thomas, D., State of the art in supply chain risk management research: Empirical and conceptual findings and a roadmap for the implementation in practice (2010) Logistics Research, 2, pp. 33-44; Pishvaee, M.S., Torabi, S.A., A possibilistic programming approach for closed-loop supply chain network design under uncertainty (2010) Fuzzy Sets and Systems, 161 (20), pp. 2668-2683; Torabi, S.A., Hassini, E., An interactive possibilistic programming approach for multiple objective supply chain master planning (2008) Fuzzy Sets and Systems, 159 (2), pp. 193-214; Troutt, M.D., Ambrose, P.J., Chan, C.K., Multistage efficiency tools for goal setting and monitoring in Supply Chains (2004) Successful Strategies in Supply Chain Management, , C.K. Chan H.W.J. Lee Idea Group Publishing Co. Hershey; Xu, J., Liu, Q., Wang, R., A class of multiobjective supply chain networks optimal model under random fuzzy environment and its application to the industry of Chinese liquor (2008) Information Sciences, 178 (8), pp. 2022-2043; Yang, F., Wu, D., Liang, L., Bi, G., Wu, D.D., Supply Chain DEA: Production possibility set and performance evaluation model (2011) Annals of Operations Research, 185, pp. 195-211; Zimmermann, H.-J., (2001) Fuzzy Set Theory and Its Applications, , 4th Ed Kluwer Academic Publishers Boston/Dordrecht/London 2001}, document_type={Conference Paper}, source={Scopus}, }`

- Bevilacqua, V., Carnimeo, L., Guccione, P., Mastronardi, G., Uva, A. E., Fiorentino, M., Monno, G., Marino, F., Dotoli, M., Costantino, N., Dassisti, M. & Carbonara, N. (2015) A multimodal system for nonverbal human feature recognition in emotional framework IN ACM International Conference Proceeding Series., 19-24.

[Bibtex]`@CONFERENCE{Bevilacqua201519, author={Bevilacqua, V. and Carnimeo, L. and Guccione, P. and Mastronardi, G. and Uva, A.E. and Fiorentino, M. and Monno, G. and Marino, F. and Dotoli, M. and Costantino, N. and Dassisti, M. and Carbonara, N.}, title={A multimodal system for nonverbal human feature recognition in emotional framework}, journal={ACM International Conference Proceeding Series}, year={2015}, volume={2015-September}, pages={19-24}, doi={10.1145/2809643.2809645}, note={cited By 1}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84947423101&doi=10.1145%2f2809643.2809645&partnerID=40&md5=72e90f3bd43f54d629785ef122ef80e8}, abstract={A correct recognition of nonverbal expressions is currently one of the most important challenges of research in the field of human computer interaction. The ability to recognize human actions could change the way to interact with machines in several environments and contexts, or even the way to live. In this paper, we describe the advances of a previous study finalized to design, implement and validate an innovative recognition system already developed by some of the authors. It was aimed at recognizing two opposite emotional conditions (resonance and dissonance) of a candidate to a job position interacting with the recruiter during a job interview. Results in terms of the accuracy, resonance rate, and dissonance rate of the three new optimized neural networkbased (NN) classifiers are discussed. Comparison with previous results of three NN classifiers is also presented based on three single domains: facial, vocal and gestural. © 2015 held by the owner/author(s).}, author_keywords={Facial/vocal/gestural features; Job interview; Neural networks and support vector machines; Nonverbal emotional recognition}, keywords={Computer programming, Emotional recognition; Facial/vocal/gestural features; Feature recognition; Job interviews; Multimodal system; Non-verbal human; Recognition systems; Single domains, Human computer interaction}, references={Battocchi, A., Pianesi, F., Goren-Bar, D., A first evaluation study of a database of kinetic facial expressions (dafex) (2005) Proceedings of the 7th International Conference on Multimodal Interfaces (2005), pp. 214-221; Bevilacqua, V., Ambruoso, D.D., Mandolino, G., Suma, M., A new tool to support diagnosis of neurological disorders by means of facial expressions (2011) Medical Measurements and Applications Proceedings (MeMeA), 2011 IEEE International Workshop on (2011), pp. 544-549; Bevilacqua, V., Barone, D., Cipriani, F., D'Onghia, G., Mastrandrea, G., Mastronardi, G., Suma, M., D'Ambruoso, D., A new tool for gestural action recognition to support decisions in emotional framework (2014) Innovations in Intelligent Systems and Applications (INISTA) Proceedings, 2014 IEEE International Symposium on (2014), pp. 184-191; Bevilacqua, V., Filograno, G., Mastronardi, G., Face detection by means of skin detection (2008) Advanced Intelligent Computing Theories and Applications. with Aspects of Artificial Intelligence, pp. 1210-1220. , Springer; Bevilacqua, V., Guccione, P., Mascolo, L., Pazienza, P.P., Salatino, A.A., Pantaleo, M., First progresses in evaluation of resonance in staff selection through speech emotion recognition (2013) ICIC, pp. 658-671. , LNAI 7996; Bevilacqua, V., Salatino, A.A., Di Leo, C., D'Ambruoso, D., Suma, M., Barone, D., Tattoli, G., Pantaleo, M., Evaluation of resonance in staff selection through multimedia contents (2014) ICIC 2014, LNAI, 8589, pp. 185-198; Burkhardt, F., Paeschke, A., Rolfes, M., Sendlmeier, W.F., Weiss, B., A database of German emotional speech (2005) Interspeech (2005), pp. 1517-1520; Cohen, I., Li, H., Inference of human postures by classification of 3D human body shape (2003) Analysis and Modeling of Faces and Gestures, 2003. AMFG 2003 IEEE International Workshop on (2003), pp. 74-81; Corradini, A., Boehme, H.-J., Gross, H.-M., Visual-based posture recognition using hybrid neural networks (1999) ESANN (1999), pp. 81-86; Ekman, P., Friesen, W.V., Ellsworth, P., (2013) Emotion in the Human Face: Guidelines for Research and An Integration of Findings, , Elsevier; Fernandes, T., Miranda, J., Alvarez, X., Orvalho, V., LIFE is-GAME-an interactive serious game for teaching facial expression recognition (2011) Interfaces (2011), pp. 1-2; Gunes, H., Shan, C., Chen, S., Tian, Y., Bodily expression for automatic affect recognition (2015) Emotion Recognition: A Pattern Analysis Approach. (2015), pp. 343-377; Hong, J.-W., Han, M.-J., Song, K.-T., Chang, Xf.-Y., A fast learning algorithm for robotic emotion recognition (2007) Computational Intelligence in Robotics and Automation, 2007. CIRA 2007. International Symposium on (2007), pp. 25-30; Jabloun, F., Erzin, E., Teager energy based feature parameters for speech recognition in car noise (1999) Signal Processing Letters IEEE, 6 (10), pp. 259-261. , 1999; Jamshidnezhad, A., Nordin, M., Challenging of facial expressions classification systems: Survey, critical considerations and direction of future work (2012) Research Journal of Applied Sciences, 4. , 2012; Kendon, A., Gesticulation and speech: Two aspects of the process of utterance (1980) The Relationship of Verbal and Nonverbal Communication, 25, pp. 207-227. , 1980; De Meijer, M., The contribution of general features of body movement to the attribution of emotions (1989) Journal of Nonverbal Behavior, 13 (4), pp. 247-268. , 1989; Metallinou, A., Katsamanis, A., Narayanan, S., Tracking continuous emotional trends of participants during affective dyadic interactions using body language and speech information (2013) Image and Vision Computing, 31 (2), pp. 137-152. , 2013; Mo, H.-C., Leou, J.-J., Lin, C.-S., Human behavior analysis using multiple 2D features and multicategory support vector machine (2009) MVA (2009), pp. 46-49; Nwe, T.L., Foo, S.W., De Silva, L.C., Speech emotion recognition using hidden Markov models (2003) Speech Communication, 41 (4), pp. 603-623. , 2003; Scherer, K.R., Johnstone, T., Klasmeyer, G., Vocal expression of emotion (2003) Handbook of Affective Sciences. (2003), pp. 433-456; Scherer, S., Hofmann, H., Lampmann, M., Pfeil, M., Rhinow, S., Schwenker, F., Palm, G., Emotion recognition from speech: Stress experiment (2008) LREC (2008); Ververidis, D., Kotropoulos, C., Emotional speech recognition: Resources, features, and methods (2006) Speech Communication, 48 (9), pp. 1162-1181. , 2006; Ververidis, D., Kotropoulos, C., Pitas, I., Automatic emotional speech classification (2004) Acoustics, Speech, and Signal Processing, 2004. Proceedings.(ICASSP'04) IEEE International Conference on (2004), pp. I-593; Viola, P., Jones, M., Rapid object detection using a boosted cascade of simple features (2001) Computer Vision and Pattern Recognition, 2001. CVPR 2001. Proceedings of the 2001 IEEE Computer Society Conference on (2001), pp. I-511; Wu, C., Aghajan, H., Model-based human posture estimation for gesture analysis in an opportunistic fusion smart camera network (2007) Advanced Video and Signal Based Surveillance, 2007. AVSS 2007 IEEE Conference on (2007), pp. 453-458; Wu, J., Pan, G., Zhang, D., Qi, G., Li, S., Gesture recognition with a 3-d accelerometer (2009) Ubiquitous Intelligence and Computing, pp. 25-38. , Springer; Zhao, H., Liu, Z., Zhang, H., Recognizing human activities using non-linear SVM decision tree (2011) Intelligent Computing and Information Science, pp. 82-92. , Springer; Mastronardi, G., Bevilacqua, V., Depasquale, R.F., Dellisanti Fabiano Vilardi, M., Attention control during distance learning sessions (2013) New Trends in Image Analysis and Processing-ICIAP, pp. 545-549; Bevilacqua, V., Mastronardi, G., Menolascina, F., Pannarale, P., Pedone, A., A novel multi-objective genetic algorithm approach to artificial neural network topology optimisation: The breast cancer classification problem (2006) Neural Networks 2006. IJCNN'06. International Joint Conference on, pp. 1958-1965. , IEEE}, document_type={Conference Paper}, source={Scopus}, }`

- Piconese, A., Bourdeaud’Huy, T., Dotoli, M. & Hammadi, S. (2015) A mathematical programming model for the real time traffic management of railway networks under disturbances. IN Communications in Computer and Information Science, 509.215-234.

[Bibtex]`@ARTICLE{Piconese2015215, author={Piconese, A. and Bourdeaud’Huy, T. and Dotoli, M. and Hammadi, S.}, title={A mathematical programming model for the real time traffic management of railway networks under disturbances}, journal={Communications in Computer and Information Science}, year={2015}, volume={509}, pages={215-234}, doi={10.1007/978-3-319-17509-6_15}, note={cited By 0}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84929650069&doi=10.1007%2f978-3-319-17509-6_15&partnerID=40&md5=dd152439a8b3d9703f8defaeba394970}, abstract={The real-time traffic management allows to solve unexpected disturbances that occur along a railway line during the normal development of the traffic. After a disturbance, the original timetable is restored through the rescheduling process. Despite the improvements of off-line decision support tools for trains dispatchers that enable a better use of rail infrastructure, real-time traffic management received a limited scientific attention. In this paper, we deal with the real time traffic management for regional railway networks, mainly single tracked, in which a centralized traffic control system is installed. The rescheduling problem is presented as a Mixed Integer Linear Programming Model which resolution allows to carry out the rescheduling process in a very short computational time. © Springer International Publishing Switzerland 2015.}, author_keywords={Centralized traffic control; Mixed Integer Linear Programming; Railway systems; Real-time optimization; Regional networks; Single-tracked}, keywords={Decision support systems; Integer programming; Mathematical programming; Operations research; Railroads; Rails; Real time systems; Traffic control; Transportation, Centralized traffic controls; Mixed integer linear programming; Railway system; Real-time optimization; Regional networks; Single-tracked, Railroad transportation}, references={D’Ariano, A., Real-time train dispatching: Models, algorithms and applications (2008) Faculty of Civil Engineering and Geosciences, Delft University of Technology, Department of Transport and Planning, , Ph.D. Thesis; Assad, A., Models for rail transportation (1980) Transp. Res. Part A, 14A, pp. 205-220; Cordeau, J.F., Toth, P., Vigo, D., A survey of optimization models for train routing and scheduling (1998) Transp. Sci, 32, pp. 380-404; Tornquist, J., Computer-based decision support for railway traffic scheduling and dispatching: A review of models and algorithms (2005) Proceedings of ATMOS 2005, , Algorithmic MeThods and Models for Optimization of RailwayS), Palma de Mallorca, Spain; Tornquist, J., Persson, J.A., N-tracked railway traffic re-scheduling during disturbances (2007) Transp. Res. Part B, 41, pp. 342-362; Ismail, S., Railway traffic control and train scheduling based on inter-train conflict management (1999) Transp. Res. Part B, 33, pp. 511-534; Dotoli, M., Epicoco, N., Falagario, M., Piconese, A., Sciancalepore, F., Turchiano, B., A real time traffic management model for regional railway networks under disturbances (2013) 9Th Annual IEEE Conference on Automation Science and Engineering, Madison, USA; (2013) Ferrovie Del Sud Est E Servizi Automobilistici, , http://www.fseonline.it; Vicuna, G., (1989) Organizzazione E Tencica Ferroviaria, 2. , CIFI; Collegio Ingegneri Ferroviari Italiani, Roma}, document_type={Conference Paper}, source={Scopus}, }`

- Dotoli, M., Epicoco, N., Falagario, M. & Sciancalepore, F. (2015) A cross-efficiency fuzzy Data Envelopment Analysis technique for performance evaluation of Decision Making Units under uncertainty. IN Computers and Industrial Engineering, 79.103-114.

[Bibtex]`@ARTICLE{Dotoli2015103, author={Dotoli, M. and Epicoco, N. and Falagario, M. and Sciancalepore, F.}, title={A cross-efficiency fuzzy Data Envelopment Analysis technique for performance evaluation of Decision Making Units under uncertainty}, journal={Computers and Industrial Engineering}, year={2015}, volume={79}, pages={103-114}, doi={10.1016/j.cie.2014.10.026}, note={cited By 91}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84911930890&doi=10.1016%2fj.cie.2014.10.026&partnerID=40&md5=921a7a2499b61f6594a4201bba8f8d73}, abstract={The paper presents a novel cross-efficiency fuzzy Data Envelopment Analysis (DEA) technique for evaluating different elements (Decision Making Units or DMUs) under uncertainty. In order to evaluate the performance of several DMUs while dealing with uncertain input and output data, the presented technique employs triangular fuzzy numbers. A fuzzy triangular efficiency is associated to each DMU through a cross evaluation obtained by a compromise between suitably chosen objectives. Results are then defuzzified to provide a ranking of the DMUs. The proposed method is applied to the performance evaluation of healthcare systems in a region of Southern Italy. The DMU data uncertainty derives from ongoing reforms and the reported assessment is conducted firstly in order to evaluate and rank the efficiency of the considered healthcare systems, and subsequently to assess the evolution of the performance of one of the most affected among these DMUs by the reform plans. The case study demonstrates the model ease of application, its discriminative power among DMUs when compared to a more classical fuzzy DEA approach, and the usefulness in planning and validating targeted reforms in the case of healthcare systems. © 2014 Elsevier Ltd.}, author_keywords={Analysis; Cross-efficiency; Data; Decision making; Envelopment; Fuzzy logic; Performance evaluation; Uncertainty}, keywords={Data envelopment analysis; Efficiency; Fuzzy logic; Fuzzy sets; Health care; Uncertainty analysis, Analysis; Cross-efficiency; Data; Envelopment; Performance evaluation; Uncertainty, Decision making}, references={Aksezer, C.S., Benneyan, J.C., Assessing the efficiency of hospitals operating under a unique owner: A DEA application in the presence of missing data (2010) International Journal of Services and Operations Management, 7 (1), pp. 53-75; Amin, G.R., Toloo, M., Finding the most efficient DMUs in DEA: An improved integrated model (2007) Computers & Industrial Engineering, 52 (2), pp. 71-77; Amin, G.R., Comments on finding the most efficient DMUs in DEA: An improved integrated model (2009) Computers and Industrial Engineering, 56, pp. 1701-1702; Andersen, P., Petersen, N.C., A procedure for ranking efficient units in data envelopment analysis (1993) Management Science, 39, pp. 1261-1264; Angulo Meza, L., Pereira Estellita Lins, M., Review of methods for increasing discrimination in data envelopment analysis (2002) Annals of Operations Research, 116 (14), pp. 225-242; Aristovnik, A., Measuring relative efficiency in health and education sector: The case of East European countries (2012) Actual Problems of Economics, 136, pp. 305-314; Barros, C.P., De Menezes, A.G., Peypoch, N., Solonandrasana, B., Vieira, J.C., An analysis of hospital efficiency and productivity growth using the Luenberger indicator (2008) Health Care Management Science, 11 (4), pp. 373-381; Barros, C.P., De Menezes, A.G., Vieira, J.C., Measurement of hospital efficiency, using a latent class stochastic frontier model (2013) Applied Economics, 45 (1), pp. 47-54; Basso, A., Funari, S., Constant and variable returns to scale DEA models for socially responsible investment funds (2014) European Journal of Operational Research, 235, pp. 775-783; Bevilacqua, V., Costantino, N., Dotoli, M., Falagario, M., Sciancalepore, F., Strategic design and multi-objective optimization of distribution networks based on genetic algorithms (2012) International Journal of Computer Integrated Manufacturing, 25 (12), pp. 1139-1150; Bryce, C.L., Engberg, J.B., Wholey, D.R., Comparing the agreement among alternative models in evaluating HMO efficiency (2000) Health Services Research, 35 (2), pp. 509-528; Büyüközkan, G., Çifçi, G., Güleryüz, S., Strategic analysis of healthcare service quality using fuzzy AHP methodology (2011) Expert Systems with Applications, 38, pp. 9407-9424; Charnes, A., Cooper, W.W., Rhodes, E., Measuring the efficiency of decision making units (1978) European Journal of Operational Research, 2, pp. 429-444; Costantino, N., Dotoli, M., Falagario, M., Fanti, M.P., Mangini, A.M., Sciancalepore, F., A hierarchical optimization technique for the strategic design of distribution networks (2013) Computers & Industrial Engineering, 6 (4), pp. 849-864; Costantino, N., Dotoli, M., Falagario, M., Fanti, M.P., Mangini, A.M., A model for supply management of agile manufacturing supply chains (2012) International Journal of Production Economics, 135 (1), pp. 451-457; Costantino, N., Dotoli, M., Falagario, M., Sciancalepore, F., Balancing the additional costs of purchasing and the vendor set dimension to reduce public procurement costs (2012) Journal of Purchasing and Supply Management, 18 (3), pp. 189-198; Costantino, N., Dotoli, M., Epicoco, N., Falagario, M., Sciancalepore, F., A novel fuzzy Data Envelopment Analysis methodology for performance evaluation in a two-stage supply chain (2012) Proc. 8th IEEE International Conference on Automation Science and Engineering (CASE 2012), pp. 974-979. , August 20-24, 2012 Seoul, Korea; Costantino, N., Dotoli, M., Falagario, M., Fanti, M.P., Mangini, A.M., Sciancalepore, F., A model for the optimal design of the hospital drug distribution system (2010) Proc. IEEE WHCM2010 Workshop, p. 6. , 18-20 February Venice, Italy; Costantino, N., Dotoli, M., Falagario, M., Fanti, M.P., Mangini, A.M., Sciancalepore, F., Optimal design of distribution networks: An application to the hospital drug distribution system (2010) Proc. ORAHS, , July 18-23. Genoa, Italy; De Boer, L., Labro, E., Morlacchi, P., A review of methods supporting supplier selection (2001) European Journal of Purchasing and Supply Management, 7, pp. 75-89; De Nicola, A., Gitto, S., Mancuso, P., Uncover the predictive structure of healthcare efficiency applying a bootstrapped Data Envelopment Analysis (2012) Expert Systems with Applications, 39, pp. 10495-10499; Doyle, J., Green, R., Efficiency and cross-efficiency in DEA: Derivation, meanings and uses (1994) Journal of Operational Research Society, 45, pp. 567-578; Dotoli, M., Falagario, M., A hierarchical model for optimal supplier selection in multiple sourcing contexts (2012) International Journal of Production Research, 50 (11), pp. 2953-2967; Emrouznejad, A., Shale, E., A combined neural network and DEA for measuring efficiency of large scale datasets (2009) Computers & Industrial Engineering, 56, pp. 249-254; Falagario, M., Sciancalepore, F., Costantino, N., Pietroforte, R., Using a DEA-Cross efficiency approach in public procurement tenders (2012) European Journal of Operational Research, 218, pp. 523-529; Fanti, M.P., Mangini, A.M., Dotoli, M., Ukovich, W., A three level strategy for the design and performance evaluation of hospital departments (2013) IEEE Transactions on Systems Man and Cybernetics Part A, 43 (4), pp. 742-756; Fetter, R.B., Shin, Y., Freeman, J.L., Averill, R.F., Thompson, J.D., Case mix definition by diagnosis-related groups (1980) Medical Care, 18 (2), pp. 1-53; GLPK Reference Manual, , http://www.gnu.org/software/glpk/glpk.html; Guo, P., Tanaka, H., Fuzzy DEA: A perceptual evaluation method (2001) Fuzzy Sets and Systems, 119 (1), pp. 149-160; Halme, M., Joro, T., Korhonen, P., Salo, S., Wallenius, J., A value efficiency approach to incorporating preference information in data envelopment analysis (1999) Management Science, 45 (1), pp. 103-115; Hatami-Marbini, A., Saati, S., Makui, A., Ideal and anti-ideal decision making units: A fuzzy DEA approach (2010) Journal of Industrial Engineering International, 6 (10), pp. 31-41; Hatami-Marbini, A., Emrouznejad, A., Tavana, M., A taxonomy and review of the fuzzy data envelopment analysis literature: Two decades in the making (2011) European Journal of Operational Research, 214, pp. 457-472; Hu, H.H., Qi, Q., Yang, C.H., Analysis of hospital technical efficiency in China: Effect of health insurance reform (2012) China Economic Review, 23 (4), pp. 865-877; Jahanshahloo, G.R., Sanei, M., Rostamy-Malkhalifeh, M., Saleh, H., A comment on "a fuzzy DEA/AR approach to the selection of flexible manufacturing systems" (2009) Computers & Industrial Engineering, 56, pp. 1713-1714; Jimenez, M., Bilbao, A., Pareto-optimal solution in fuzzy multi-objective linear programming (2009) Fuzzy Sets and Systems, 160, pp. 2714-2721; Kabak Ö., Ülengin, F., Possibilistic linear-programming approach for supply chain networking decisions (2011) European Journal of Operational Research, 209 (3), pp. 253-264; Kao, C., Liu, S.-T., Efficiencies of two-stage systems with fuzzy data (2011) Fuzzy Sets and Systems, 176 (1), pp. 20-35; Khodabakhshi, M., Gholami, Y., Kheirollahi, H., An additive model approach for estimating returns to scale in imprecise data envelopment analysis (2010) Applied Mathematical Modelling, 34 (5), pp. 1247-1257; Leon, T., Liern, V., Ruiz, J.L., Sirvent, I., A fuzzy mathematical programming approach to the assessment of efficiency with DEA models (2003) Fuzzy Sets and Systems, 139 (2), pp. 407-419; Lertworasirikul, S., Fang, S.C., Joines, J.A., Nuttle, H.L.W., Fuzzy data envelopment analysis (DEA): A possibility approach (2003) Fuzzy Sets and Systems, 139 (2), pp. 379-394; Lertworasirikul, S., Fang, S.C., Joines, J.A., Nuttle, H.L.W., Fuzzy data envelopment analysis (fuzzy DEA): A credibility approach (2003) Fuzzy Sets Based Heuristics for Optimization, pp. 141-158. , J.L. Verdegay, Physica Verlag; Levitt, M.S., Joyce, M.A.S., (1987) The Growth and Efficiency of Public Spending, , Cambridge University Press; Li, X.-B., Reeves, G.R., A multiple criteria approach to data envelopment analysis (1999) European Journal of Operational Research, 115 (3), pp. 507-517; Li, L., Zabinsky, Z.B., Incorporating uncertainty into a supplier selection problem (2011) International Journal of Production Economics, 134, pp. 344-356; Liang, T.F., Application of fuzzy sets to manufacturing/distribution planning decisions in supply chains (2011) Information Science, 181, pp. 842-854; Liu, S.T., A fuzzy DEA/AR approach to the selection of flexible manufacturing systems (2008) Computers and Industrial Engineering, 54 (1), pp. 66-76; Ma, C., Liu, D., Zhou, Z., Zhao, W., Liu, W., Game cross efficiency for systems with two-stage structures (2014) Journal of Applied Mathematics, 2014, p. 8; Markovits-Somogyi, R., Ranking efficient and inefficient decision making units in data envelopment analysis (2011) International Journal for Traffic and Transport Engineering, 1 (4), pp. 245-256; Mukherjee, K., Santerre, R.E., Zhang, N.J., Explaining the efficiency of local health departments in the U.S.: An exploratory analysis (2010) Health Care Management Science, 13, pp. 378-387; Pedraja-Chaparro, F., Salinas-Jimenez, J., Smith, P., On the role of weight restrictions in data envelopment analysis (1997) Journal of Productivity Analysis, 8, pp. 215-230; Qin, R., Liu, Y.K., A new data envelopment analysis model with fuzzy random inputs and outputs (2009) Journal of Applied Mathematics and Computing, 33 (12), pp. 327-356; Qin, R., Liu, Y.K., Modeling data envelopment analysis by chance method in hybrid uncertain environments (2010) Mathematics and Computers in Simulation, 80 (5), pp. 922-950; Saati, S., Menariani, A., Jahanshahloo, G.R., Efficiency analysis and ranking of DMUs with fuzzy data (2002) Fuzzy Optimization and Decision Making, 1, pp. 255-267; Sengupta, J.K., A fuzzy systems approach in data envelopment analysis (1992) Computers and Mathematics with Applications, 24 (89), pp. 259-266; Sexton, T.R., Silkman, R.H., Hogan, A.J., Data envelopment analysis: Critique and extensions (1986) Measuring Efficiency: An Assessment of Data Envelopment Analysis, , R.H. Silkman, Jossey-Bass San Francisco, CA; Sheth, N., Triantis, K., Measuring and evaluating efficiency and effectiveness using goal programming and data envelopment analysis in a fuzzy environment (2003) Yugoslav Journal of Operations Research, 13 (1), pp. 35-60; Shiraz, R.K., Charles, V., Jalalzadeh, L., Fuzzy rough DEA model: A possibility and expected value approaches (2014) Expert Systems with Applications, 41 (2), pp. 434-444; Sulku, S.N., The health sector reforms and the efficiency of public hospitals in Turkey: Provincial markets (2012) European Journal of Public Health, 22, pp. 634-638; Tiemann, O., Schreyögg, J., Changes in hospital efficiency after privatization (2012) Health Care Management Science, 15, pp. 310-326; Toloo, M., The most efficient unit without explicit inputs: An extended MILP-DEA model (2013) Measurement, 46 (9), pp. 3628-3634; Toloo, M., Selecting and full ranking suppliers with imprecise data: A new DEA method (2014) The International Journal of Advanced Manufacturing Technology; Toloo, M., Kresta, A., Finding the best asset financing alternative: A DEA-WEO approach (2014) Measurement, 55, pp. 288-294; Tsai, H.Y., Chang, C.W., Lin, H.L., Fuzzy hierarchy sensitive with Delphi method to evaluate hospital organization performance (2010) Expert Systems with Applications, 37, pp. 5533-5541; Ufuk Bilsel, R., Ravindran, A., A multiobjective chance constrained programming model for supplier selection under uncertainty (2011) Transportation Research, Part B: Methodological, 45 (8), pp. 1284-1300; Van Leekwijck, W., Kerre, E.E., Defuzzication: Criteria and classification (1999) Fuzzy Sets and Systems, 108, pp. 159-178; Wang, Y.M., Luo, Y., DEA efficiency assessment using ideal and anti-ideal decision-making units (2006) Applied Mathematics and Computation, 173, pp. 902-915; Wang, Y.M., Luo, Y., Liang, L., Fuzzy data envelopment analysis based upon fuzzy arithmetic with an application to performance assessment of manufacturing enterprises (2009) Expert Systems with Applications, 36 (3), pp. 5205-5211; Wang, Y.M., Chin, K.S., A neutral DEA model for cross-efficiency evaluation and its extension (2010) Expert Systems with Applications, 37 (5), pp. 3666-3675; Wei, C.K., Chen, L.C., Li, R.K., Tsai, C.H., Using the DEA-R model in the hospital industry to study the pseudo-inefficiency problem (2011) Expert Systems with Applications, 38, pp. 2172-2176; Worthington, A.C., Frontier efficiency measurement in health care: A review of empirical techniques and selected applications (2004) Medical Care Research and Review, 61 (2), pp. 135-170; Zadeh, L.A., Fuzzy sets (1965) Information and Control, 8 (3), pp. 338-353; Zerafat Angiz, L.M., Emrouznejad, M., Mustafa, A., Fuzzy data envelopment analysis: A discrete approach (2012) Expert Systems with Applications, 39 (3), pp. 2263-2269; Zerafat Angiz, L.M., Emrouznejad, M., Mustafa, A., Fuzzy assessment of performance of a decision making units using DEA: A non-radial approach (2010) Expert Systems with Applications, 37 (7), pp. 5153-5157; Zhang, X., Zhang, L., Supplier selection and purchase problem with fixed cost and constrained order quantities under stochastic demand (2011) International Journal of Production Economics, 129 (1), pp. 1-7; Zhou, Z., Yang, W., Maa, C., Liu, W., A comment on 'A comment on 'A fuzzy DEA/AR approach to the selection of flexible manufacturing systems' and 'A fuzzy DEA/AR approach to the selection of flexible manufacturing systems' (2010) Computers & Industrial Engineering, 59, pp. 1019-1021; Zhou, Z., Zhao, L., Lui, S., Ma, C., A generalized fuzzy DEA/AR performance assessment model (2012) Mathematical and Computer Modelling, 55 (1112), pp. 2117-2128; Zhu, J., Data Envelopment Analysis with Preference Structure (1996) The Journal of the Operational Research Society, 47 (1), pp. 136-150; Zimmermann, H.-J., (2001) Fuzzy Set Theory and Its Applications, , 4th Ed. Kluwer Academic Publishers Boston/Dordrecht/London}, document_type={Article}, source={Scopus}, }`

### 2014

- Bevilacqua, V., Dotoli, M., Foglia, M. M., Acciani, F., Tattoli, G. & Valori, M. (2014) Artificial neural networks for feedback control of a human elbow hydraulic prosthesis. IN Neurocomputing, 137.3-11.

[Bibtex]`@ARTICLE{Bevilacqua20143, author={Bevilacqua, V. and Dotoli, M. and Foglia, M.M. and Acciani, F. and Tattoli, G. and Valori, M.}, title={Artificial neural networks for feedback control of a human elbow hydraulic prosthesis}, journal={Neurocomputing}, year={2014}, volume={137}, pages={3-11}, doi={10.1016/j.neucom.2013.05.066}, note={cited By 9}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84899638087&doi=10.1016%2fj.neucom.2013.05.066&partnerID=40&md5=d9d0dcfdb1b0874d1405ce613f59dcf7}, abstract={The paper addresses feedback control of actuated prostheses based on the Stewart platform parallel mechanism. In such a problem it is essential to apply a feasible numerical method to determine in real time the solution of the forward kinematics, which is highly nonlinear and characterized by analytical indetermination. In this paper, the forward kinematics problem for a human elbow hydraulic prosthesis developed by the research group of Polytechnic of Bari is solved using artificial neural networks as an effective and simple method to obtain in real time the solution of the problem while limiting the computational effort. We show the effectiveness of the technique by designing a PID controller that governs the arm motion thanks to the provided neural computation of the forward kinematics. © 2014 Elsevier B.V.}, author_keywords={Artificial neural networks; Control; Forward kinematics; Human prosthesis; Parallel mechanism; Simulation}, keywords={Control; Feedback control; Mechanisms; Neural networks; Simulators, Computational effort; Forward kinematics; Forward kinematics problem; Neural computations; Parallel mechanisms; Research groups; Simulation; Stewart platforms, Prosthetics}, references={Bevilacqua, V., 3D virtual colonoscopy for automatic polyps detection by artificial neural network approach: new tests on an enlarged cohort of polyps (2013) Neurocomputing, 116, pp. 62-75; Chen, N., Song, S., Direct position analysis of the 4-6 Stewart platforms (1994) ASME J. Mech. Des., 116, pp. 61-66; Dehghani, M., Ahmadi, M., Khayatian, A., Eghtesad, M., Farid, M., Neural network solutions for forward kinematics problem of HEXA parallel robot Proceedings of the American Control Conference, pp. 4214-4219. , July 2008; Dhingra, A.K., Almadi, A.N., Kohli, D., A Grobner-Sylvester hybrid method for closed-form displacement analysis of mechanisms (2000) J. Mech. Des., 122, pp. 431-438; Fite, K.B., Wait, K.W., Withrow, T.J., Shen, X., Mitchell, J.E., Goldfarb, M., A gas-actuated anthropomorphic prosthesis for transhumeral amputees (2008) IEEE Trans. Robotics, 24, pp. 159-169; Foglia, M.M., Valori, M., A wired actuated elbow for human prosthesis (2011) UPB Sci. Bull. Ser. D Mech. Eng., 73, pp. 49-58; Geng, Z., Haynes, L., Neural network solution for the forward kinematics problem of a Stewart platform (1991) Proceedings of the International Conference on Robotics and Automation, pp. 2650-2655. , April; Griffis, M., Duffy, J., A forward displacement analysis of a class of Stewart platforms (1989) J. Robot. Syst., 6, pp. 703-720; Haykin, S., (1998) Neural Networks: A Comprehensive Foundation, , 2nd edition; Huang, D.S., (1996) Systematic Theory of Neural Networks for Pattern Recognition, , Publishing House of Electronic Industry of China, Beijing; Huang, D.S., The local minima free condition of feedforward neural networks for outer-supervised learning (1998) IEEE Trans. Syst. Man Cybern. Part B, 28, pp. 477-480; Huang, D.S., Radial basis probabilistic neural networks: model and application (1999) Int. J. Pattern Recog. Artif. Intell., 13 (7), pp. 1083-1101; Huang, D.S., Du, J.-X., A constructive hybrid structure optimization methodology for radial basis probabilistic neural networks (2008) IEEE Trans. Neural Netw., 19 (12), pp. 2099-2115; Huang, D.S., Ma, S.D., A new radial basis probabilistic neural network model (1996) Proceedings of the 3rd International Conference on Signal Processing (ICSP), pp. 1449-1452. , October 14-18, 1996, Beijing, China; Huang, D.S., Ma, S.D., Linear and nonlinear feedforward neural network classifiers: a comprehensive understanding (1999) J. Intell. Syst., 9, pp. 1-38; Huang, D.S., Zhao, W.-B., Determining the centers of radial basis probabilities neural networks by recursive orthogonal least square algorithms (2005) Appl. Math. Comput., 162 (1), pp. 461-473; Huang, X.G., He, G.P., New and efficient method for the forward kinematics solution of the general planar Stewart platform (2009) Proceedings of the IEEE International Conference on Automation and Logistics, p. 5; Huang, X.G., Liao, Q.Z., Wei, S.M., Xu, Q., Huang, S.G., The 4SPS-2CCS generalized Stewart-Gough platform mechanism and its direct kinematics (2007) Proceedings of the IEEE International Conference on Mechatronics and Automation, pp. 2472-2477. , August; Huang, X.G., Liao, Q.P., Wei, S.M., Xu, Q., Huang, S.G., Forward kinematics of the 6-6 Stewart platform with planar base and platform using algebraic elimination (2007) Proceedings of the IEEE International Conference on Automation and Logistics, pp. 2655-2659. , August; Innocenti, C., Direct kinematics in analytical form of the 6-4 fully parallel mechanism (1995) ASME J. Mech. Des., 117, pp. 89-95; Innocenti, C., Parenti Castelli, V., Direct position analysis of the Stewart platform mechanism (1990) Mech. Mach. Theory, 25, pp. 611-621; Innocenti, C., Parenti Castelli, V., A novel numerical approach to the closure of the 6-6 Stewart platform mechanism (1991) Proceedings of the 5th International Conference on Advanced Robotics, IEEE ICAR'91, pp. 851-855. , June; Lee, H.S., Han, M.-C., The estimation for forward kinematics solution of Stewart platform using the neural network (1999) Proceedings of the International Conference on Intelligent Robots and Systems, pp. 501-506; Lin, W., Crane, C., Duffy, J., Closed-form forward analysis of the 4-5 in-parallel platforms (1994) ASME J. Mech. Des., 116, pp. 47-53; Lin, W., Griffis, M., Duffy, J., Forward displacement analyses of the 4-4 Stewart platforms (1990) Proceedings of the 21st ASME Mechanisms Conference, pp. 263-269; Mendoza-Vázquez, J.R., Tlelo-Cuautle, E., Vázquez-Gonzalez, J.L., Escudero-Uribe, A.Z., Simulation of a parallel mechanical elbow with 3 DOF (2009) J. Appl. Res. Technol., 7, pp. 113-123; http://www.utaharm.com/ua3-myoelectric-arm.php, Motion Control, Utah arm; Nielson, J.B., Roth The direct kinematics of the general 6-5 Stewart-Gough mechanism (1996) Recent Advances in Robot Kinematics, Kluwer Academic Publishers, pp. 7-16; Ren, L., Feng, Z.R., Mills, J.K., A self-tuning iterative calculation approach for the forward kinematics of a Stewart-Gough platform (2006) Proceedings of the IEEE International Conference on Mechatronics and Automation, pp. 2018-2023. , Dordrecht, The Netherlands; Tarokh, M., Real time forward kinematics solutions for general Stewart platforms (2007) Proceedings of the IEEE International Conference on Robotics and Automation, pp. 901-906. , April; Wen, F., Liang, C., Displacement analysis of the 6-6 Stewart platform mechanisms (1994) Mech. Mach. Theory, 29, pp. 547-557}, document_type={Article}, source={Scopus}, }`

- Danielis, R., Dotoli, M., Fanti, M. P., Mangini, A. M., Pesenti, R., Stecco, G. & Ukovich, W. (2014) Integrating ICT into Logistics Intermodal Systems: A petri net model of the Trieste port IN 2009 European Control Conference, ECC 2009., 4769-4774.

[Bibtex]`@CONFERENCE{Danielis20144769, author={Danielis, R. and Dotoli, M. and Fanti, M.P. and Mangini, A.M. and Pesenti, R. and Stecco, G. and Ukovich, W.}, title={Integrating ICT into Logistics Intermodal Systems: A petri net model of the Trieste port}, journal={2009 European Control Conference, ECC 2009}, year={2014}, pages={4769-4774}, doi={10.23919/ecc.2009.7075154}, art_number={7075154}, note={cited By 1}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84955199195&doi=10.23919%2fecc.2009.7075154&partnerID=40&md5=65f8221d56181b5a7bade81cb6eb5f82}, abstract={The paper focuses on the issue of the modeling and management of Logistics Intermodal Systems (LIS) integrated by ICT (Information and Communication Technologies) tools. To this aim, we consider as a case study the port of Trieste (Italy) and we model the system in a Petri net framework. The port logistics and the truck traffic are described in different operative conditions characterized by different levels of ICT integration and information sharing between infrastructures and operators. Moreover, the system is simulated in the Matlab environment under different traffic scenarios and system capacity assumptions. The simulation results show that ICT have a huge potential for efficient real time management and operation of LIS. © 2009 EUCA.}, keywords={MATLAB; Petri nets; Ports and harbors, ICT integrations; Information and Communication Technologies; Information sharing; Intermodal system; MATLAB environment; Petri net models; Real-time management; System Capacity, Information management}, references={Caris, A., Macharis, C., Janssens, G.K., Planning problems in intermodal freight transport: Accomplishments and prospects (2008) Transportation Planning and Technology, 31 (3), pp. 277-302; Cassandras, C.G., Lafortune, S., (2008) Introduction to Discrete Event Systems, , Second Edition, New York, NY, USA: Springer; Chen, H., Labadi, K., Amodeo, L., Modeling, analysis, and optimization of logistics systems Petri net based approaches (2006) Proc. Int. Conf. on Service Systems and Service Management, pp. 575-582; Degano, C., Di Febbraro, A., Modelling automated material handling in intermodal terminals (2001) Proc. IEEE/ASME Int. Conf. Advanced Intelligent Mechatronics, 2, pp. 1023-1028; Dotoli, M., Fanti, M.P., Iacobellis, G., Mangini, A.M., A first order hybrid petri net model for supply chain management (2009) IEEE Transactions on Automation Science and Engineering; Di Febbraro, A., Porta, G., Sacco, N., A Petri net modelling approach of intermodal terminals based on Metrocargo system (2006) Intelligent Transportation Systems Conf., pp. 1442-1447; Transport intermodality, inter-modality and intermodal freight transport in the European Union-a systems approach to freight transport (1997) Communication from the Commission to the European Parliament and the Council, , European Commission Task Force; Fischer, M., Kemper, P., Modeling and analysis of a freight terminal with stochastic Petri nets (2000) Proc. 9th IFAC Symposium Control in Transportation Systems, , June 13-15; Gambardella, L.M., Rizzoli, A.E., Zaffalon, M., Simulation and planning of an intermodal container terminal (1998) Simulation, 71, pp. 107-116; Giannopoulos, G.A., The application of information and communication technologies in transport (2004) Eur. J. Oper. Res., 152, pp. 302-320; Maione, G., Ottomanelli, M., A preliminary Petri net model of the transshipment processes in the Taranto container terminal (2005) 10th IEEE Int. Conf. Emerging Technologies and Factory Automation (ETFA'05), 1, pp. 165-171; Peterson, J.L., (1981) Petri Net Theory and the Modeling of Systems, , Prentice Hall, Englewood Cliffs, NJ, USA; Ramstedt, L., Woxenius, J., Modelling approaches to operational decision-making in freight transport chains (2006) 18th NOFOMA Conf., 16p; Verbraeck, A., Versteegt, C., Logistic control for fully automated large-scale freight transport systems (2001) IEEE Conf. Intelligent Transportation Systems, pp. 770-775; Viswanadham, N., Analysis of manufacturing enterprises: An approach to leveraging value delivery (1999) Processes for Competitive Advantage, , Boston, MA: Kluwer Academic; Viswanadham, N., Raghavan, S., Performance analysis and design of supply chain: A Petri net approach (2000) J. Oper. Res. Soc, 51 (10), pp. 1158-1169; Xu, J., Hancock, K.L., Enterprise-wide freight simulation in an integrated logistics and transportation System (2004) IEEE Trans. Int. Transp. Sys., 5, pp. 342-346}, document_type={Conference Paper}, source={Scopus}, }`

- Dotoli, M., Fanti, M. P., Iacobellis, G. & Rotunno, G. (2014) An integrated technique for the internal logistics analysis and management in discrete manufacturing systems. IN International Journal of Computer Integrated Manufacturing, 27.165-180.

[Bibtex]`@ARTICLE{Dotoli2014165, author={Dotoli, M. and Fanti, M.P. and Iacobellis, G. and Rotunno, G.}, title={An integrated technique for the internal logistics analysis and management in discrete manufacturing systems}, journal={International Journal of Computer Integrated Manufacturing}, year={2014}, volume={27}, number={2}, pages={165-180}, doi={10.1080/0951192X.2013.802370}, note={cited By 9}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84890121908&doi=10.1080%2f0951192X.2013.802370&partnerID=40&md5=c70041fa7c1c279e6da5613a202224e7}, abstract={A novel hierarchical and iterative technique is presented for the analysis and management of the internal logistics of manufacturing systems. The method effectively integrates the value stream mapping (VSM) tool, the analytic hierarchy process (AHP) approach, and discrete event simulation. Starting from a concise description of the manufacturing system obtained by the VSM graphical approach, so as to identify nonvalue-adding activities, a detailed and standardised description is obtained by the unified modelling language (UML). Then the AHP technique is used to rank the system anomalies, singling out the major ones. Further application of the VSM tool produces an overall picture of the desired manufacturing system internal flow, and the UML description details the to-be system activities. Finally, the use of discrete event simulation allows the quantitative verification of the effects of the changes in the production system. The proposed technique is a tool to systematically improve the internal logistics of complex production systems while assessing the system dynamics and corresponding performance improvements. An application of the method to a real case study enlightens its effectiveness. © 2013 Taylor & Francis.}, author_keywords={analytic hierarchy process; discrete event simulation; internal logistics; manufacturing systems; unified modelling language; value stream mapping}, keywords={Analytic hierarchy process; Computer hardware description languages; Computer simulation languages; Hierarchical systems; Iterative methods; Manufacture; Mapping; Modeling languages; Unified Modeling Language, Analytic hierarchy process (ahp); Complex production systems; Discrete manufacturing systems; Integrated techniques; Internal Logistics; Iterative technique; Quantitative verification; Value stream mapping, Discrete event simulation}, references={Abdulmalek, F.A., Rajgopal, J., Analyzing the benefits of lean manufacturing and value stream mapping via simulation: A process sector case study (2007) International Journal of Production Economics, 107 (1), pp. 223-236. , DOI 10.1016/j.ijpe.2006.09.009, PII S0925527306002258, Building Core-Competence Through Operational Excellence; Agyapong-Kodua, K., Ajaefobi, J.O., Weston, R.H., Modelling dynamic value streams in support of process design and evaluation (2009) International Journal of Computer Integrated Manufacturing, 22 (5), pp. 411-427; Álvarez, R., Calvo, R., Pena, M., Domingo, R., Redesigning an assembly line through lean manufacturing tools (2009) International Journal of Advanced Manufacturing Technology, 43 (9-10), pp. 949-958; Banks, J., (2005) Discrete-event System Simulation, , Upper Saddle River, NJ: Prentice-Hall; Bonney, M., Jaber, M.Y., Developing an input-output activity matrix (ioam) for environmental and economic analysis of manufacturing systems and logistics chains (2012) International Journal of Production Economics, , doi:10.1016/j.ijpe.2011.12.016; Braglia, M., Carmignani, G., Zammori, F., A new value stream mapping approach for complex production systems (2006) International Journal of Production Research, 44 (18-19), pp. 3929-3952. , DOI 10.1080/00207540600690545, PII V80533272016Q178; Christopher, M., (2010) Logistics and Supply Chain Management: Creating Value-Adding Networks, , 4th ed. Edinburgh: Prentice Hall; Costantino, N., Dotoli, M., Falagario, M., Fanti, M.P., Mangini, A.M., A model for supply management of agile manufacturing supply chains (2012) International Journal of Production Economics, 135 (1), pp. 451-457; Deif, A., Computer simulation to manage lean manufacturing systems (2010) Proceedings of the 2nd International Conference on Computer Engineering and Technology (ICCET), pp. 677-681. , Regina, SK, Canada, April 16-18; Detty, R.B., Yingling, J.C., Quantifying benefits of conversion to lean manufacturing with discrete event simulation: A case study (2000) International Journal of Production Research, 38 (2), pp. 429-445. , DOI 10.1080/002075400189509; Dotoli, M., Fanti, M.P., Coloured timed petri net model for real time control of agv systems (2004) International Journal of Production Research, 42 (1-9), pp. 1787-1814; Dotoli, M., Fanti, M.P., A coloured Petri net model for automated storage and retrieval systems serviced by rail-guided vehicles: A control perspective (2005) International Journal of Computer Integrated Manufacturing, 18 (2-3), pp. 122-136. , DOI 10.1080/0951192052000288233; Dotoli, M., Fanti, M.P., Giua, A., Seatzu, C., First-order hybrid petri nets. An application to distributed manufacturing systems (2008) Nonlinear Analysis: Hybrid Systems, 2 (2), pp. 408-430; Fanti, M.P., Maione, B., Piscitelli, G., Turchiano, B., System approach to design generic software for real-time control of flexible manufacturing systems (1996) IEEE Transactions on Systems, Man, and Cybernetics Part A:Systems and Humans., 26 (2), pp. 190-202. , PII S1083442796013926; Gourgand, M., Lacomme, P., Traoré, M.K., Design of a monitoring environment for manufacturing systems management and optimization (2003) International Journal of Computer Integrated Manufacturing, 16 (1), pp. 61-80; Hines, P., Rich, N., The seven value stream mapping tools (1997) International Journal of Operations & Production Management, 17 (1), pp. 46-64; Kelton, W.D., Sadowski, R.P., Swets, N.B., (2009) Simulation with Arena, , 5th ed. Boston, MA: Mc Graw-Hill; Koh, S.C.L., Chung, W.W.C., Gunasekaran, A., A special issue on tools and techniques for dynamic manufacturing environments (2006) International Journal of Computer Integrated Manufacturing, 19 (1), pp. 1-3. , DOI 10.1080/09511920500174976, PII R61550842541368; Li, W.D., McMahon, C.A., A simulated annealing-based optimization approach for integrated process planning and scheduling (2007) International Journal of Computer Integrated Manufacturing, 20 (1), pp. 80-95. , DOI 10.1080/09511920600667366, PII K065652240N11240; Lian, Y.-H., Van Landeghem, H., Analysing the effects of Lean manufacturing using a value stream mapping-based simulation generator (2007) International Journal of Production Research, 45 (13), pp. 3037-3058. , DOI 10.1080/00207540600791590, PII 778790434; McDonald, T., Van Aken, E.M., Rentes, A.F., Utilizing simulation to enhance value stream mapping: A manufacturing case application (2002) International Journal of Logistics: Research and Applications, 5 (2), pp. 213-232; McManus, H.L., Millard, R.L., Value stream analysis and mapping for product development (2002) Proceedings of the International Council of the Aeronautical Sciences, pp. 8-13. , 23rd ICAS Congress, Toronto, Canada, September; Melouk, S.H., Freeman, N.K., Miller, D., Dunning, M., Simulation optimization-based decision support tool for steel manufacturing (2013) International Journal of Production Economics, 141 (1), pp. 269-276; Miles, R., Hamilton, K., (2006) Learning UML 2.0., , Sebastopol, CA: O'Reilly Media; Narasimhan, J., Parthasarathy, L., Narayan, P.S., Increasing the effectiveness of value stream mapping using simulation tools in engine test operations (2007) Proceedings of the 18th IASTED International Conference on Modelling and Simulation, , Montreal, QC, Canada, May 30-June 1; Pan, G.-Q., Feng, D.-Z., Jiang, M.-X., Application research of shortening delivery time through value stream mapping analysis (2010) Proceedings of the IEEE 17th International Conference on Industrial Engineering and Engineering Management, pp. 29-31. , Hangzhou, China, October; Papakostas, N., Alexopoulos, K., Kopanakis, A., Integrating digital manufacturing and simulation tools in the assembly design process: A cooperating robots cell case (2011) CIRP Journal of Manufacturing Science and Technology, 4 (1), pp. 96-100; Rabelo, L., Eskandari, H., Shaalan, T., Helal, M., Value chain analysis using hybrid simulation and AHP (2007) International Journal of Production Economics, 105 (2), pp. 536-547. , DOI 10.1016/j.ijpe.2006.05.011, PII S0925527306001289, Scheduling in Batch-Processing Industries and Supply Shains; Ramesh, V., Sreenivasa Prasad, K.V., Srinivas, T.R., Implementation of a lean model for carrying out value stream mapping in a manufacturing industry (2008) Journal of Industrial and Systems Engineering, 2, pp. 180-196; Saaty, T.L., Decision making with the analytic hierarchy process (2008) International Journal of Services Sciences, 1 (1), pp. 83-98; Seth, D., Gupta, V., Application of value stream mapping for lean operations and cycle time reduction: An Indian case study (2005) Production Planning and Control, 16 (1), pp. 44-59. , DOI 10.1080/09537280512331325281; Teilans, A., Kleins, A., Merkuryev, Y., Grinbergs, A., Design of uml models and their simulation using arena (2008) WSEAS Transactions on Computer Research, 3, pp. 67-73; Triantaphyllou, E., Chi-Tun, L., Development and evaluation of five fuzzy multiattribute decision-making methods (1996) International Journal of Approximate Reasoning, 14 (4), pp. 281-310. , DOI 10.1016/0888-613X(95)00119-2; Womack, J.P., Jones, D.T., (2003) Lean Thinking: Banish Waste and Create Wealth in Your Corporation, , 2nd ed. New York: Free Press; Yan, P., Zhou, M.C., A life-cycle engineering approach to development of flexible manufacturing systems (2003) IEEE Transactions on Robotics and Automation, 19 (3), pp. 465-473; Yan, P., Zhou, M.C., Sebastian, D., A generic framework for integrated product and process development (1998) International Journal of Environmentally Conscious Design and Manufacturing, 7 (3), pp. 47-57; Yan, P., Zhou, M.C., Sebastian, D., A methodology for integrated product and process development: Concept formulation (1999) Robotics and Computer-Integrated Manufacturing, 15, pp. 201-210; Yan, P., Zhou, M.C., Sebastian, D., Caudill, R., Integrating eco-compass concepts into integrated product and process development (2001) International Journal of Environmentally Conscious Design and Manufacturing, 10 (3), pp. 6-16}, document_type={Article}, source={Scopus}, }`

- Dotoli, M., Epicoco, N., Falagario, M. & Sciancalepore, F. (2014) Supplier evaluation and selection under uncertainty via an integrated model using cross-efficiency Data Envelopment Analysis and Monte Carlo simulation IN 19th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2014..

[Bibtex]`@CONFERENCE{Dotoli2014, author={Dotoli, M. and Epicoco, N. and Falagario, M. and Sciancalepore, F.}, title={Supplier evaluation and selection under uncertainty via an integrated model using cross-efficiency Data Envelopment Analysis and Monte Carlo simulation}, journal={19th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2014}, year={2014}, doi={10.1109/ETFA.2014.7005102}, art_number={7005102}, note={cited By 2}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84983146247&doi=10.1109%2fETFA.2014.7005102&partnerID=40&md5=c5da4b3d399574a5e6fc8539516dbba6}, abstract={This paper addresses a key objective of the supply chain strategic design, i.e., the optimal selection of suppliers under uncertainty. A methodology integrating the cross-efficiency Data Envelopment Analysis and the Monte Carlo approach is proposed. Their combination allows overcoming the deterministic feature of the classical cross-efficiency DEA. Moreover, we define an indicator of the robustness of the determined supplier ranking. The resulting technique allows managing the supplier selection problem while considering nondeterministic input and output data, a significant circumstance for assessing potential suppliers, with which there are no previous commercial relationships. The approach helps buyers in choosing the right partners under uncertainty and ranking them upon a multiple sourcing strategy. © 2014 IEEE.}, keywords={Data envelopment analysis; Efficiency; Factory automation; Intelligent systems; Supply chains; Uncertainty analysis, Commercial relationship; Input and outputs; Integrated modeling; Monte Carlo approach; Optimal selection; Sourcing strategies; Supplier evaluation and selections; Supplier selection, Monte Carlo methods}, references={Araz, C., Ozkarahan, I., Supplier evaluation and management system for strategic sourcing based on a new multicriteria sorting procedure (2007) Int. J. Prod. Econ., 106, pp. 585-606; Bevilacqua, V., Costantino, N., Dotoli, M., Falagario, M., Sciancalepore, F., Strategic design and multiobjective optimization of distribution networks based on genetic algorithms (2012) Int. J. Comp. Int. Manuf., 25 (12), pp. 1139-1150; Charnes, A., Cooper, W.W., Rhodes, E., Measuring the efficiency of decision making units (1978) Eur. J. Oper. Res., 2, pp. 429-444; Costantino, N., Dotoli, M., Falagario, M., Fanti, M.P., Iacobellis, G., A decision support system framework for purchasing management in supply chains (2009) J. Bus. Ind. Mark., 24 (3-4), pp. 278-290; Costantino, N., Dotoli, M., Falagario, M., Fanti, M.P., Mangini, A.M., Sciancalepore, F., Ukovich, W., A model for the optimal design of the hospital drug distribution chain (2010) Proc. WHCM 2010, , 18-20. 02. 2010 Venezia (Italy); Costantino, N., Dotoli, M., Epicoco, N., Falagario, M., Sciancalepore, F., A cross efficiency fuzzy Data Envelopment Analysis technique for supplier evaluation under uncertainty 17th ETFA Conf., , 17-21. 09. 2012, Krakòw (Poland), 2012a; Costantino, N., Dotoli, M., Falagario, M., Fanti, M.P., Mangini, A.M., A model for supply management of agile manufacturing supply chains (2012) Int. J. Prod. Econ., 135 (1), pp. 451-457; Costantino, N., Dotoli, M., Epicoco, N., Falagario, M., Sciancalepore, F., A novel fuzzy Data Envelopment Analysis methodology for performance evaluation in a two-stage supply chain (2012) 8th CASE Conf., , 20-24. 08. 2012, Seoul (Korea); Costantino, N., Dotoli, M., Falagario, M., Sciancalepore, F., Balancing the additional costs of purchasing and the vendor set dimension to reduce public procurement costs (2012) J. Purch. Suppl. Manag., 18, pp. 189-198; Costantino, N., Dotoli, M., Falagario, M., Fanti, M.P., Mangini, A.M., Sciancalepore, F., Ukovich, W., A hierarchical optimization technique for the strategic design of distribution networks (2013) Comput. Ind. L Eng., 68, pp. 849-864; Dotoli, M., Falagario, M., A hierarchical model for optimal supplier evaluation and selection in multiple sourcing contexts (2012) Int. J. Prod. Res., 11, pp. 1-15; Doyle, J.R., Green, R.H., Cross-Evaluation in DEA: Improving discrimination among DMUs (1995) Infor., 33 (3), pp. 205-222; Dyson, R.G., Shale, E.A., Data envelopment analysis, operational research and uncertainty (2010) J. Oper. Res. Soc., 61, pp. 25-34; Falagario, M., Sciancalepore, F., Costantino, N., Pietroforte, R., Using a DEA-cross efficiency approach in public procurement tenders (2012) Eur. J. Oper. Res., 218, pp. 523-529; Ho, W., Xu, X., Dey, P.K., Multi-criteria decision making approaches for supplier evaluation and selection: A literature review (2010) Eur. J. Oper. Res., 202 (1), pp. 16-24; Kao, C., Liu, T.S., Stochastic data envelopment analysis in measuring the efficiency of Taiwan commercial banks (2009) Eur. J. Oper. Res., 196, pp. 312-322; Kuo, R.J., Wang, Y.C., Tien, F.C., Integration of artificial neural network and MADA methods for green supplier selection (2010) J. Clean. Prod., 18 (12), pp. 1161-1170; Law, A.M., Kelton, W.D., (2000) Simulation Modeling and Analysis, , McGraw-Hill, New York, 4th ed; Sexton, T.R., Silkman, R.H., Hogan, A.J., Data Envelopment Analysis: Critique and extensions (1986) Measuring Efficiency: An Assessment of Data Envelopment Analysis, , R. H. Silkman (Ed.), San Francisco, CA: Jossey-Bass; Vose, D., (2008) Risk Analysis: A Quantitative Guide, , John Wiley and Sons, 2nd ed; Wong, W.P., Jaruphongsa, W., Lee, L.H., Supply chain performance measurement system: A Monte Carlo DEA-based approach (2008) Int. J. Industrial and Systems Eng., 3, pp. 162-188}, document_type={Conference Paper}, source={Scopus}, }`

- Carli, R., Deidda, P., Dotoli, M. & Pellegrino, R. (2014) An urban control center for the energy governance of a smart city IN 19th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2014..

[Bibtex]`@CONFERENCE{Carli2014, author={Carli, R. and Deidda, P. and Dotoli, M. and Pellegrino, R.}, title={An urban control center for the energy governance of a smart city}, journal={19th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2014}, year={2014}, doi={10.1109/ETFA.2014.7005155}, art_number={7005155}, note={cited By 11}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84946686880&doi=10.1109%2fETFA.2014.7005155&partnerID=40&md5=7ae375bfea52c5c6212722aca93bc04d}, abstract={The paper addresses the emerging need of providing urban managers with tools for energy governance of smart cities. We present the architecture of a decision support system called Urban Control Center (UCC). The UCC measures the city energy performance and supports the decision maker in determining the optimal action plan for implementing smartness strategies in the city energy governance. To this aim, the UCC relies on a two-level decentralized programming model that integrates several decision making units (decision panels), each focusing on the energy optimization of a specific urban subsystem. © 2014 IEEE.}, keywords={Artificial intelligence; Decision support systems; Factory automation, Control center; Decision makers; Decision making unit; Energy optimization; Energy performance; Optimal actions; Programming models; Smart cities, Decision making}, references={Abdoullaev, A., A smart world: A development model for intelligent cities (2011) Proc. 11th IEEE Int. Conf. Comp. Inf. Tech.; Adckison, D., (2013) IBM Cognos Business Intelligence, , Packt Publishing; Adepetu, A., Grogan, P., Alfaris, A., Svetinovic, D., De Weck, O., City. Net IES: A sustainability-oriented energy decision support system (2012) Proc. IEEE SysCon2012, pp. 1-7; Belissent, J., (2011) The Core of A Smart City Must Be Smart Governance, , Cambridge: Forrester Research; Bhowmick, A., Francellino, E., Glehn, L., Loredo, R., Nesbitt, P., Yu, S.W., Mishr, S., (2012) IBM Intelligent Operations Center for Smarter Cities Administration Guide IBM Redbooks; Caputo, P., Costa, G., Ferrari, S., A supporting method for defining energy strategies in the building sector at urban scale (2013) Ener. Pol., 55, pp. 261-270; Carli, R., Dotoli, M., Pellegrino, R., Ranieri, L., Measuring and managing the smartness of cities: A framework for classifying performance indicators (2013) Proc. IEEE SMC2013, pp. 1288-1293; Coutinho-Rodrigues, J., Simãoa, A., Henggeler Antunes, C., A GIS-based multicriteria spatial decision support system for planning urban infrastructures (2011) Dec. Supp. Sys., 51 (3), pp. 720-726; Dall'O, G., Norese, M.F., Galante, A., Novello, C., A multi-criteria methodology to support public administration decision making concerning sustainable energy action plans (2013) Ener., 6 (8), pp. 4308-4330; Trends and Projections in Europe 2013-Tracking Progress Towards Europe's Climate and Energy Targets until, , http://www.eea.europa.eu/publications/trends-and-projections-2013, EEA-European Environment Agency; Fatta, D., Naoum, D., Loizidou, M., Integrated environmental monitoring and simulation system for use as a management decision support tool in urban areas (2002) J. Environm. Man., 64 (4), pp. 333-343; Figueira, J., Greco, S., Ehrgott, M., (2005) Multiple Criteria Decision Analysis: State of the Art Surveys, , Springer, Boston; Fiorucci, P., Minciardi, R., Robba, M., Sacile, R., Solid waste management in urban areas: Development and application of a decision support system (2003) Resour. Conserv. Rec., 37 (4), pp. 301-328; Juan, Y.-K., Wang, L., Wang, J., Leckie, J.O., Li, K.-M., A decision-support system for smarter city planning and management (2011) IBM J. Res. Develop., 55 (1-2), pp. 31-12; Keirstead, J., Jennings, M., Sivakumar, A., A review of urban energy system models: Approaches, challenges and opportunities (2012) Ren. Sust. Ener. Rev., 16, pp. 3847-3866; Marler, R.T., Arora, J.S., Survey of multi-objective optimization methods for engineering (2004) Structur. Multidisc. Optim., 26 (6), pp. 369-395; McCormick, K., Abbott, D., Brown, M.S., Khabaza, T., Mutchler, S.R., (2013) IBM SPSS Modeler Cookbook, , Packt Publishing; Naphade, M., Banavar, G., Harrison, C., Paraszczak, J., Morris, R., Smarter cities and their innovation challenges (2011) Computer, 44 (6), pp. 32-39; Pearson, L.J., Coggan, A., Proctor, W., Smith ., T.F., A sustainable decision support framework for urban water management (2010) Water Resour. Manag., 24, pp. 363-376; Quintero, A., Konaré, D., Pierre, S., Prototyping an intelligent decision support system for improving urban infrastructures management (2005) Eur. J. Op. Res., 162 (3), pp. 654-672; Stoilov, T., Stoilova, K., (1999) Noniterative Coordination in Multilevel Systems, , Kluwer; Vicente, L.N., Calamai, P.H., Bilevel and multilevel programming: A bibliography review (1994) J. Glob. Optim., 5 (3), pp. 291-306}, document_type={Conference Paper}, source={Scopus}, }`

- Carli, R. & Dotoli, M. (2014) Energy scheduling of a smart home under nonlinear pricing IN Proceedings of the IEEE Conference on Decision and Control., 5648-5653.

[Bibtex]`@CONFERENCE{Carli20145648, author={Carli, R. and Dotoli, M.}, title={Energy scheduling of a smart home under nonlinear pricing}, journal={Proceedings of the IEEE Conference on Decision and Control}, year={2014}, volume={2015-February}, number={February}, pages={5648-5653}, doi={10.1109/CDC.2014.7040273}, art_number={7040273}, note={cited By 34}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84961994657&doi=10.1109%2fCDC.2014.7040273&partnerID=40&md5=55539508a30611826d5d2e0f0b11c853}, abstract={The paper focuses on the scheduling of energy activities in smart homes equipped with controllable electrical appliances, renewable energy sources, dispatchable energy generators, and energy storage systems. We formulate a mixed integer quadratic programming energy scheduling algorithm for cost minimization under nonlinear pricing. The scheduling technique manages the use of electrical appliances, plans the energy production and supplying, and programs the storage systems charging/discharging. A case study simulated in different scenarios demonstrates that the approach allows full exploitation of the potential of local energy generation and storage to reduce the individual user energy consumption costs, while complying with the customer energy needs. © 2014 IEEE.}, references={Achterberg, T., SCIP: Solving constraint integer programs (2009) Math. Progr. Comp., 1, pp. 1-41; Barbato, A., Capone, A., Carello, G., Delfanti, M., Merlo, M., Zaminga, A., Cooperative and non-cooperative house energy optimization in a smart grid perspective (2011) Proc. IEEE Int. Symp. World of Wireless, Mobile and Multimedia Networks, 6p; Carli, R., Dotoli, M., Pellegrino, R., Ranieri, L., Measuring and managing the smartness of cities: A framework for classifying performance indicators (2013) Proc. IEEE Conf. Systems, Man and Cybernetics (SMC 2013), pp. 1288-1293; Di Giorgio, A., Pimpinella, L., Liberati, F., A model predictive control approach to the load shifting problem in a household equipped with an energy storage unit (2012) Proc. MED 2012 Conf, pp. 1491-1498; Dotoli, M., Epicoco, N., Falagario, M., Sciancalepore, F., Costantino, N., A nash equilibrium simulation model for the competitiveness evaluation of the auction based day ahead electricity market (2014) Comp. Ind., 65 (4), pp. 774-785; Gatsis, N., Giannakis, G.B., Residential demand response with interruptible tasks: Duality and algorithms (2011) Proc. 50th IEEE CDC-IFAC ECC Int. Conf., pp. 1-6; Holland, S., Mansur, E., The short-run effects of time-varying prices in competitive electricity markets (2006) Ener. J., 27, pp. 127-155; Hubert, T., Grijalva, S., Realizing smart grid benefits requires energy optimization algorithms at residential level (2011) Proc. Innovative Smart Grid Technologies, 8p; Integration of demand side management, distributed generation, renewable energy sources and energy storages (2008) IEA Demand Side Man. Prog. Tech. Rep.; Ioakimidis, C.S., Eliasstam, H., Rycerski, P., Solar power forecasting of a residential location as part of a smart grid structure (2012) Proc. IEEE Int. Conf. Energytech, 6p; Kailas, A., Cecchi, V., Mukherjee, A., A survey of contemporary technologies for smart home energy management (2013) Green Information and Communication Systems Handbook, , Academic Press; Kok, K., Karnouskos, S., Nestle, D., Dimeas, A., Weidlich, A., Warmer, C., Strauss, P., Lioliou, V., Smart houses for a smart grid (2009) Proc. Int. Conf. on Electricity Distribution CIRED, 4p; Mohsenian-Rad, A.-H., Leon-Garcia, A., Optimal residential load control with price prediction in real-time electricity pricing environments (2010) IEEE Trans. Smart Grids, 1, pp. 120-133; Mohsenian-Rad, A.-H., Wong, V.W.S., Jatskevich, J., Schober, R., Optimal and autonomous incentive-based energy consumption scheduling algorithm for smart grid (2010) Proc. Innovative Smart Grid Technologies, 8p; Molitor, C., Togawa, K., Bolte, S., Monti, A., Load models for home energy system and micro grid simulations (2012) Proc. Innovative Smart Grid Technologies, 6p; Sun, B., Luh, P.B., Jia, Q.-S., Jiang, Z., Wang, F., Song, C., Building energy management: Integrated control of active and passive heating, cooling, lighting, shading, and ventilation systems (2013) IEEE Trans. Aut. Sci. Eng., 10 (3), pp. 588-602; Tsui, K.M., Chan, S.C., Demand response optimi ation for smart home scheduling under real-time pricing (2012) IEEE Trans. Smart Grids, 3 (4), pp. 1812-1821; University of Auckland, OPTI - A Free MATLAB Toolbox for Optimization, , http://www.i2c2.aut.ac.nz/Wiki/OPTI/, Available at; Wacks, K.P., Utility load management using home automation (1991) IEEE Trans. Cons. Electr., 37, pp. 168-174; Walt, R.R., The BioMax/spl trade/new biopower option for distributed generation and CHP (2004) Proc. IEEE Power Engineering Soc. Gen. Meet., 2, pp. 1653-1656}, document_type={Conference Paper}, source={Scopus}, }`

- Dotoli, M., Epicoco, N., Falagario, M. & Cavone, G. (2014) A timed Petri nets model for intermodal freight transport terminals IN IFAC Proceedings Volumes (IFAC-PapersOnline)., 176-181.

[Bibtex]`@CONFERENCE{Dotoli2014176, author={Dotoli, M. and Epicoco, N. and Falagario, M. and Cavone, G.}, title={A timed Petri nets model for intermodal freight transport terminals}, journal={IFAC Proceedings Volumes (IFAC-PapersOnline)}, year={2014}, volume={9}, number={3}, pages={176-181}, doi={10.3182/20140514-3-FR-4046.00038}, note={cited By 10}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84945963822&doi=10.3182%2f20140514-3-FR-4046.00038&partnerID=40&md5=12d63035451f5fdd5d932509425ee237}, abstract={This paper presents a general modelling framework for intermodal freight transport terminals. The model allows evaluating the operational performance of such transportation systems, assessing the efficiency level of the terminal and identifying its bottlenecks by suitable performance indices. Moreover, it allows evaluating different solutions to the identified criticalities. The proposed framework is modular and based on timed Petri nets: places represent resources and capacities or conditions, transitions model inputs, flows and activities into the terminal, and tokens are intermodal transport units or the means on which they are transported. A simulation of a case study shows the model effectiveness. © IFAC.}, author_keywords={Discrete event systems; Intermodal transport; Modelling; Simulation; Timed Petri nets}, keywords={Freight transportation; Intermodal transportation; Models; Petri nets; Traffic control, Intermodal freight transport; Intermodal transport; Modelling framework; Operational performance; Simulation; Timed Petri Net; Timed Petri nets models; Transportation system, Discrete event simulation}, references={Bevilacqua, V., Costantino, N.N., Dotoli, M., Falagario, M., Sciancalepore, F., Strategic design and multi-objective optimization of distribution networks based on genetic algorithms (2012) Int. J. Comp. Int. Manuf., 25 (12), pp. 1139-1150; Boschian, V., Dotoli, M., Fanti, M.P., Iacobellis, G., Ukovich, W., A metamodelling approach to the management of intermodal transportation networks (2011) IEEE Trans. Aut. Sci. Eng., 8 (3), pp. 457-469; Costantino, N., Dotoli, M., Falagario, M., Fanti, M.P., Mangini, A.M., A model for supply management of agile manufacturing supply chains (2012) Int. J. Prod. Econ., 135 (1), pp. 451-457; David, R., Alla, H., (2005) Discrete, Continuous, and Hybrid Petri Nets, , Berlin Heidelberg: Springer-Verlag; Di Febbraro, A., Porta, G., Sacco, N., A Petri net modelling approach of intermodal terminals based on Metrocargo system (2006) Proc. 9th Intelligent Transportation Systems Conf., pp. 1442-1447. , Toronto (Canada); Dotoli, M., Epicoco, N., Falagario, M., Seatzu, C., Turchiano, B., Optimization of intermodal rail-road freight transport terminals (2014) Proc. IEEE Int. Conf. Rob. Autom. ICRA2014, 6p. , Hong Kong, China; Dotoli, M., Sciancalepore, F., Epicoco, N., Falagario, M., Turchiano, B., Costantino, N., A periodic event scheduling approach for offline timetable optimization of regional railways (2013) Proc. ICNSC2013, 6p. , Paris, France; Dotoli, M., Fanti, M.P., Mangini, A.M., Stecco, G., Ukovich, W., The impact of ICT on intermodal transportation systems: A modelling approach by Petri nets (2010) Control Eng. Pract., 18 (8), pp. 893-903; Eng-Larsson, F., Kohn, C., Modal shift for greener logistics - The shipper's perspective (2012) Int. J. Phys. Distrib. Logist. Manag., 42 (1), pp. 36-59; Fischer, M., Kemper, P., Modeling and analysis of a freight terminal with stochastic Petri nets (2000) Proc. 9th IFAC Symposium Control in Transportation Systems, , Braunschweig (Germany); Giua, A., DiCesare, F., Silva, M., Generalized mutual exclusion constraints on nets with uncontrollable transitions (1992) Proc. 1992 IEEE Int. Conf. Sys. Man Cybern., pp. 974-979. , Chicago (U.S.); Labadi, K., Chen, H., Modelling, analysis and optimisation of supply chains by using Petri net models: The state-of-the-art (2010) Int. J. Business Performance and Supply Chain Modelling, 2 (3-4), pp. 188-215; Perego, A., Perotti, S., Mangiaracina, R., ICT for logistics and freight transportation: A literature review and research agenda (2011) Int. J. Phys. Distrib. Logist. Manag., 41 (5), pp. 457-483; Peterson, J.L., (1981) Petri Net Theory and the Modeling of Systems, , Englewood Cliffs, NJ: Prentice Hall; Sessego, F., Giua, A., Seatzu, C., HYPENS: A Matlab tool for timed discrete, continuous and hybrid Petri nets (2008) Proc. 29th Int. Conf. on Applications and Theory of Petri Nets, pp. 419-428. , Lecture Notes in Computer Science 5062, Springer-Verlag; Viswanadham, N., (1999) Analysis of Manufacturing Enterprises: An Approach to Leveraging Value Delivery Processes for Competitive Advantage, , Boston: Kluwer}, document_type={Conference Paper}, source={Scopus}, }`

- Dotoli, M., Epicoco, N., Falagario, M., Seatzu, C. & Turchiano, B. (2014) Optimization of intermodal rail-road freight transport terminals IN Proceedings – IEEE International Conference on Robotics and Automation., 1971-1976.

[Bibtex]`@CONFERENCE{Dotoli20141971, author={Dotoli, M. and Epicoco, N. and Falagario, M. and Seatzu, C. and Turchiano, B.}, title={Optimization of intermodal rail-road freight transport terminals}, journal={Proceedings - IEEE International Conference on Robotics and Automation}, year={2014}, pages={1971-1976}, doi={10.1109/ICRA.2014.6907120}, art_number={6907120}, note={cited By 9}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84929208527&doi=10.1109%2fICRA.2014.6907120&partnerID=40&md5=92706f4392f3bb5e46ee05561f09136c}, abstract={In this paper we present a decision support scheme to help managing and optimizing two critical activities in intermodal terminals, namely the containers allocation in the terminal yard and the freight trains composition. In particular, the focus of this paper is on the first problem and the goal is that of maximizing the utilization of the available space while keeping into account several constraints. The approach was successfully tested on a real case study, the rail-road terminal of a leading intermodal logistics company. © 2014 IEEE.}, references={Abacoumkin, C., Ballis, A., Development of an expert system for the evaluation of conventional and innovative technologies in the intermodal transport area (2004) European Journal of Operational Research, 152, pp. 410-419; Ambrosino, D., Bramardi, A., Pucciano, M., Sacone, S., Siri, S., Modeling and solving the train load planning problem in seaport container terminals (2011) Proc. 7th Conference on Automation Science and Engineering, pp. 208-213; Bish, E., A multiple-crane-constrained scheduling problem in a container terminal (2003) European Journal of Operational Research, 144, pp. 83-107; Bortfeldt, A., Wäscher, G., Constraints in container loading-A state of the art review (2013) European Journal of Operational Research, 229, pp. 1-20; Boschian, V., Dotoli, M., Fanti, M.P., Iacobellis, G., Ukovich, W., A metamodelling approach to the management of intermodal transportation networks (2011) IEEE Transactions on Automation Science and Engineering, 8 (3), pp. 457-469; Bostel, N., Dejax, P., Models and algorithms for container allocation problems on trains in a rapid transshipment shunting yard (1998) Transportation Science, 32 (4), pp. 370-379; Bruns, F., Knust, S., Optimized load planning of trains in intermodal transportation (2012) OR Spectrum, 34 (3), pp. 511-533; Caris, A., MacHaris, C., Jansses, G.K., Planning problems in intermodal freight transport: Accomplishments and prospects (2008) Transportation Planning and Technology, 31 (3), pp. 277-302; Cao, B., Uebe, G., Solving transportation problems with nonlinear side constraints with tabu search (1995) Computers and Operations Research, 22 (6), pp. 593-603; Corry, P., Kozan, E., An assignment model for dynamic load planning of intermodal trains (2006) Computers & Operations Research, 33, pp. 1-17; Costantino, N., Dotoli, M., Falagario, M., Fanti, M.P., Mangini, A.M., A model for supply management of agile manufacturing supply chains (2012) International Journal of Production Economics, 135 (1), pp. 451-457; Dotoli, M., Sciancalepore, F., Epicoco, N., Falagario, M., Turchiano, B., Costantino, N., A periodic event scheduling approach for offline timetable optimization of regional railways (2013) Proc. 10th IEEE International Conference on Networking, Sensing and Control (ICNSC2013), , Paris, France, April 10-12; Dotoli, M., Epicoco, N., Falagario, M., Palma, D., Turchiano, B., A train load planning optimization model for intermodal freight tran sport terminals: A case study (2013) Proc. 2013 IEEE Conf. Systems, Man and Cybernetics, , Manchester, United Kingdom, October 13-16; Dyckhoff, H., A typology of cutting and packing problems (1990) European Journal of Operational Research, 44, pp. 145-159; Ferreira, L., Sigut, J., Modelling intermodal freight terminal operations (1995) Road and Transportation Research, 4 (4), pp. 4-16; Geng, G., Li, L., Scheduling railway freight cars (2001) Knowledge-Based Systems, 14, pp. 289-297; Gnu Linear Programming, , http://www.gnu.org/software/glpk/; Kim, K., Evaluation of the number of rehandles in container yards (1997) Computers in Industrial Engineering, 32 (4), pp. 701-711; Kim, K.H., Kim, H.B., The optimal determination of the space requirements and the number of transfer cranes for import containers (1998) Computers and Industrial Engineering, 35 (3-4), pp. 673-676; Kim, K.H., Park, K.T., A note on a dynamic space-allocation method for outbound containers (2003) European Journal of Operations Research, 148 (1), pp. 92-101; Kim, K.H., Park, Y.M., Ryu, K.R., Deriving decision rules to locate export containers in container yards (2000) European Journal of Operations Research, 124, pp. 89-101; Kozan, E., Preston, P., An approach to determine storage locations of containers at seaport terminals (2001) Computers and Operations Research, 28, pp. 985-995; Marín Martínez, F., García Gutiérrez, I., Ortiz Oliveira, A., Arreche Bedia, L.M., Gantry crane operations to transfer containers between trains: A simulation study of a spanish terminal (2003) Transportation Planning and Technology, 27, pp. 261-284; Mattfeld, D., Kopfer, H., Terminal operations management in vehicle transshipment (2003) Transportation Research, Part A, 37, pp. 435-452; Stahlbock, R., Voss, S., Operations research at container terminals: A literature update (2008) OR Spectrum, 30, pp. 1-52; Zhang, C., Liu, J., Wan, Y., Murty, K.G., Linn, R.J., Storage space allocation in container terminals (2003) Transportation Research, Part B, 37, pp. 883-903}, document_type={Conference Paper}, source={Scopus}, }`

- Dotoli, M., Epicoco, N., Cavone, G., Turchiano, B. & Falagario, M. (2014) Simulation and performance evaluation of an Intermodal terminal using Petri Nets IN Proceedings – 2014 International Conference on Control, Decision and Information Technologies, CoDIT 2014., 327-332.

[Bibtex]`@CONFERENCE{Dotoli2014327, author={Dotoli, M. and Epicoco, N. and Cavone, G. and Turchiano, B. and Falagario, M.}, title={Simulation and performance evaluation of an Intermodal terminal using Petri Nets}, journal={Proceedings - 2014 International Conference on Control, Decision and Information Technologies, CoDIT 2014}, year={2014}, pages={327-332}, doi={10.1109/CoDIT.2014.6996915}, art_number={6996915}, note={cited By 3}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84921395897&doi=10.1109%2fCoDIT.2014.6996915&partnerID=40&md5=fd846395d6e7004b94407b51c1ec4da3}, abstract={This paper focuses on modelling and performance evaluation of an Intermodal Freight Transport Terminal (IFTT), the rail-road inland terminal of a leading Italian intermodal logistics company. The IFTT is regarded as a discrete event system and is modelled in a timed Petri net framework. By means of suitable performance indices, we simulate the Petri net model and evaluate the operational performance of the transport system. This allows assessing the efficiency level of the terminal and identifying its criticalities and bottlenecks. Further, the model allows evaluating different solutions to the recognized criticalities under alternative scenarios (e.g., when inflow traffic increases and congestions may occur). © 2014 IEEE.}, author_keywords={Discrete event systems; Intermodal transport terminal; Modelling; Performance evaluation; Simulation; Timed Petri nets}, keywords={Criticality (nuclear fission); Freight transportation; Intermodal transportation; Models; Petri nets; Scheduling algorithms; Traffic control, Intermodal freight transport; Intermodal terminals; Intermodal transport; Operational performance; Performance evaluation; Performance indices; Simulation; Timed Petri Net, Discrete event simulation}, references={Bhattacharya, A., Kumar, S.A., Tiwari, M.K., Talluri, S., An intermodal freight transport system for optimal supply chain logistics (2014) Transportation Research Part C, 38, pp. 73-84; (2001) Terminology on Combined Transport the European Commission (EC), , UN, New York and Geneva; Ferreira, L., Sigut, J., Modelling intermodal freight terminal operations (1995) Road and Transportation Research, 4 (4), pp. 4-16; Maione, G., Discrete-Event dynamic systems modelling distributed multi-agent control of intermodal container terminals (2008) Robotics Automation and Control, pp. 39-58. , Pecherkova P. et al. (eds.); Filipova, K., Stojadinova, T., Hadjiatanasova, V., Application of Petri Nets for transport streams modeling (2002) Facta Universitatis: Architecture and Civil Engineering, 2 (4), pp. 295-306; Labadi, K., Chen, H., Modelling, analysis and optimisation of supply chains by using Petri net models: The state-of-the-art (2010) Int. J. Business Perform. Supply Chain Modelling, 2 (3-4), pp. 188-215; Di Febbraro, A., Porta, G., Sacco, N., A Petri net modelling approach of intermodal terminals based on Metrocargo system (2006) Proc. 9th Intelligent Transportation Systems Conf., pp. 1442-1447. , Toronto (Canada); Dotoli, M., Fanti, M.P., Mangini, A.M., Stecco, G., Ukovich, W., The impact of ICT on intermodal transportation systems: A modelling approach by Petri nets (2010) Contr. Eng. Pract., 18 (8), pp. 893-903; Alicke, K., Modeling and optimization of the intermodal terminal Mega Hub (2002) OR Spectrum, 24, pp. 1-17; Boschian, V., Dotoli, M., Fanti, M.P., Iacobellis, G., Ukovich, W., A metamodelling approach to the management of intermodal transportation networks (2011) IEEE Trans. Autom. Sci. Eng., 8 (3), pp. 457-469; Cassandras, C.G., Lafortune, S., (2008) Introduction to Discrete Event Systems, , 2nd ed., Boston: Kluwer Academic; Peterson, J.L., (1981) Petri Net Theory and the Modeling of Systems, , Englewood Cliffs, NJ: Prentice Hall; Law, A.L., (2007) Simulation Modeling and Analysis, , New York (NY): McGraw-Hill; Viswanadham, N., (1999) Analysis of Manufacturing Enterprises: An Approach to Leveraging Value Delivery Processes for Competitive Advantage, , Boston: Kluwer Academic; Degano, C., Di Febbraro, A., Modelling automated material handling in intermodal terminals (2001) Proc. 2001 IEEWASME Int. Conf. Advanced Intelligent Mechatronics, , 8-12 July, Como, Italy; David, R., Alla, H., (2010) Discrete, Continuous and Hybrid Petri Nets, , Berlinheidelberg: Springer}, document_type={Conference Paper}, source={Scopus}, }`

- Dotoli, M., Epicoco, N., Turchiano, B., Falagario, M. & Sciancalepore, F. (2014) Simulation and evaluation of the auction based day ahead energy market via a Nash equilibrium model and zonal pricing IN 2014 22nd Mediterranean Conference on Control and Automation, MED 2014., 1061-1066.

[Bibtex]`@CONFERENCE{Dotoli20141061, author={Dotoli, M. and Epicoco, N. and Turchiano, B. and Falagario, M. and Sciancalepore, F.}, title={Simulation and evaluation of the auction based day ahead energy market via a Nash equilibrium model and zonal pricing}, journal={2014 22nd Mediterranean Conference on Control and Automation, MED 2014}, year={2014}, pages={1061-1066}, doi={10.1109/MED.2014.6961515}, art_number={6961515}, note={cited By 1}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84916888779&doi=10.1109%2fMED.2014.6961515&partnerID=40&md5=96eaf43cc7d748f4979f1e745df559bb}, abstract={We present a simulation model based on the Nash equilibrium for the analysis of the auction based day ahead electricity generation market. Starting from the empirical data distributions of the market clearing price and the energy demand registered by the supervisory authority, the model allows evaluating the market competitiveness and preventing anticompetitive actions by participants. It also represents a basis for a decision support tool for producers to define their optimal bidding strategy. With respect to other existing models, it allows considering differences in the generation capacities of producers, in the utilized energy sources, and in the zonal market. The model is tested in the Italian energy market by means of two different scenarios and by varying the number of bidders and their production capacities. © 2014 IEEE.}, author_keywords={auction; competitiveness; electricity market; game theory; generation companies; Nash equilibrium; simulation}, keywords={Commerce; Competition; Computation theory; Decision support systems; Game theory, auction; competitiveness; Electricity market; Generation companies; Nash equilibria; simulation, Computer simulation}, references={Nanduri, V., Das, T.K., A survey of critical research areas in the energy segment of restructured electric power markets (2009) Int. J. Elec. Power, 31, pp. 181-191; Dotoli, M., Epicoco, N., Falagario, M., Sciancalepore, F., Costantino, N., A Nash equilibrium simulation model for the competitiveness evaluation of the auction based day ahead electricity market (2014) Comp. Ind, 65, pp. 774-785; Kwon, R.H., Frances, D., Optimization-based bidding in day-Ahead electricity auction markets: A review of models for power producers (2012) Handbook of Networks in Power Systems, pp. 41-59; Ma, X.W., Sun, D.I., Cheung, K.W., Evolution toward standardized market design (2003) IEEE Trans. Power Sys, 18, pp. 460-469; De La Torre, S., Contreras, J., Conejo, A.J., Finding multiperiod nash equilibria in pool-based electricity markets (2004) IEEE Trans. Power Sys, 19, pp. 643-651; You, W.X., Xu, D.D., Li, W.W., Research on optimal bidding strategies for power suppliers based on sealed auction (2010) Proc. Int. Conf. on Power System Technology, pp. 1-5. , Hangzhou, China; Gianfreda, A., Grossi, L., Forecasting italian electricity zonal prices with exogenous variables (2012) Energ. Econ, 34, pp. 2228-2239; McAfee, R.P., McMillan, J., Auctions and bidding (1987) J. Econ. Lit, 2, pp. 699-738; Klemperer, P., Auction theory: A guide to the literature (1999) J. Econ. Surv, 13, pp. 227-286; (2013) GME, , www.mercatoelettrico.org; Li, T., Shahidehpour, M., Strategic bidding of transmissionconstrained gen-cos with incomplete information (2005) IEEE Trans. Power Sys, 20, pp. 437-447; Borenstein, S., Bushnell, S., Kahn, E., Stoft, S., Market power in california electricity markets (1995) Utilit. Policy, 5, pp. 219-236; Leeprechanon, N., David, A.K., Moorthy, S.S., Brooks, R.D., Nealand, J.H., Market power in developing countries (2002) Power Sys. Techn, 3, pp. 1805-1813; Sugianto, L.F., Liao, K.Z., Comparison of different auction pricing rules in the electricity market (2014) Modern Appl. Sci, 8, pp. 147-163; Bevilacqua, V., Costantino, N., Dotoli, M., Falagario, M., Sciancalepore, F., Strategic design and multi-objective optimization of distribution networks based on genetic algorithms (2012) Int. J. Comp. Int. Manuf, 25, pp. 1139-1150; Costantino, N., Dotoli, M., Falagario, M., Sciancalepore, F., Balancing the additional costs of purchasing and the vendor set dimension to reduce public procurement costs (2012) J. Purch. Suppl. Man, 18, pp. 189-198}, document_type={Conference Paper}, source={Scopus}, }`

- Dotoli, M., Epicoco, N., Falagario, M., Turchiano, B., Cavone, G. & Convertini, A. (2014) A Decision Support System for real-time rescheduling of railways IN 2014 European Control Conference, ECC 2014., 696-701.

[Bibtex]`@CONFERENCE{Dotoli2014696, author={Dotoli, M. and Epicoco, N. and Falagario, M. and Turchiano, B. and Cavone, G. and Convertini, A.}, title={A Decision Support System for real-time rescheduling of railways}, journal={2014 European Control Conference, ECC 2014}, year={2014}, pages={696-701}, doi={10.1109/ECC.2014.6862177}, art_number={6862177}, note={cited By 10}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84911480266&doi=10.1109%2fECC.2014.6862177&partnerID=40&md5=1a944040060d3fed8ad9f768f452b52a}, abstract={We present a Decision Support System (DSS) for real-time management of railway networks. The DSS employs a mathematical programming approach addressing traffic rescheduling under unexpected disturbances in a mixed-(single- and double-) tracked network. The DSS simulates the network behavior with the mathematical programming model based on the railway topology and constraints, rescheduling the timetable in real time, detecting and solving conflicts in the network. The DSS is applied to a real data set related to a large portion of a regional network in Southern Italy. © 2014 EUCA.}, keywords={Artificial intelligence; Mathematical programming; Railroad transportation; Railroads; Topology, Decision support system (dss); Mathematical programming models; Network behaviors; Railway network; Real data sets; Real-time management; Real-time rescheduling; Regional networks, Decision support systems}, references={Cordeau, J.F., Toth, P., Vigo, D., A survey of optimization models for train routing and scheduling (1998) Transp. Sci., 32, pp. 380-420; Corman, F., D'Ariano, A., Pacciarelli, D., Pranzo, M., Bi-objective conflict detection and resolution in railway traffic management (2012) Transp. Res. C, 20, pp. 79-94; D'Ariano, A., Pranzo, M., Hansen, I.A., Conflict resolution and train speed coordination for solving real-time timetable perturbations (2007) IEEE Trans. Int. Transp. Sys., 8 (2), pp. 208-222; Dotoli, M., Epicoco, N., Falagario, M., Piconese, A., Sciancalepore, F., Turchiano, B., A real time traffic management model for regional railway networks under disturbances (2013) Proc. CASE 2013, , Madison, USA; Dotoli, M., Hammadi, S., Jeribi, K., Russo, C., Zgaya, H., A multi-agent decision support system for optimization of co-modal transportation route planning services (2013) Proc. IEEE CDC2013, , Florence, Italy; Dotoli, M., Sciancalepore, F., Epicoco, N., Falagario, M., Turchiano, B., Costantino, N., A periodic event scheduling approach for offline timetable optimization of regional railways (2013) Proc. ICNSC 2013, , Paris, France; Krasemann, J.T., Design of an effective algorithm for fast response to the re-scheduling of railway traffic during disturbances (2012) Transp. Res., 20, pp. 62-78; Landex, A., Evaluation of railway networks with single track operation using the UIC 406 capacity method (2009) Networks and Spatial Economics, 9 (1), pp. 7-23; Sahin, I., Railway traffic control and train scheduling based on intertrain conflict management (1999) Transp. Res. B, 33, pp. 511-534; Salido, M.A., Barber, F., Ingolotti, L., Robustness in railway transportation scheduling (2008) Proc. Intell. Contr. Autom. Conf., pp. 2880-2885. , Chongqing, China; Törnquist, J., Persson, J.A., N-tracked railway traffic re-scheduling during disturbances (2007) Transp. Res. B, 41, pp. 342-362; Vromans, M.J., Dekker, R., Kroon, L.G., Reliability and heterogeneity of railway services (2006) Eur. J. Op. Res., 172, pp. 647-665}, document_type={Conference Paper}, source={Scopus}, }`

- Piconese, A., Bourdeaud’Huy, T., Dotoli, M. & Hammadi, S. (2014) A revisited model for the real time traffic management IN ICORES 2014 – Proceedings of the 3rd International Conference on Operations Research and Enterprise Systems., 139-150.

[Bibtex]`@CONFERENCE{Piconese2014139, author={Piconese, A. and Bourdeaud'Huy, T. and Dotoli, M. and Hammadi, S.}, title={A revisited model for the real time traffic management}, journal={ICORES 2014 - Proceedings of the 3rd International Conference on Operations Research and Enterprise Systems}, year={2014}, pages={139-150}, doi={10.5220/0004869701390150}, note={cited By 0}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84902323935&doi=10.5220%2f0004869701390150&partnerID=40&md5=b88d465e7539a31f982d85adced9347d}, abstract={The real-time trafficmanagement allow to solve unexpected disturbances that occur along a railway line during the normal developement of the traffic. The original timetable is restored through the rescheduling process. Despite the increase of real-time decision support tools for trains dispatchers that enable a better use of rail infrastructure, real-time traffic management received a limited scientific attention. In this paper, we deal with the real time traffic management for regional railway networks, mainly single tracks, in which a centralized traffic control system is installed. The rescheduling problem is presented as a Mixed Integer Linear Programming Model which resolution allows to carry out the rescheduling process in a very short computational time. Copyright © 2014 SCITEPRESS.}, author_keywords={Centralized traffic control; Linear programming; Railway systems; Real-time optimization; Regional networks; Single-track}, keywords={Decision support systems; Integer programming; Linear programming; Operations research; Railroad transportation; Railroads; Rails; Traffic control, Centralized traffic controls; Railway system; Real-time optimization; Regional networks; Single-tracks, Real time systems}, references={D'Ariano, A., (2008) Real-Time Train Dispatching: Models, Algorithms and Applications, , PhD thesis, Faculty of Civil Engineering and Geosciences, Delft University of Technology, Department of Transport and Planning; Dotoli, M., Epicoco, N., Falagario, M., Piconese, A., Sciancalepore, F., Turchiano, B., A real time traffic management model for regional railway networks under disturbances (2013) 9th Annual IEEE Conference on Automation Science and Engineering, , Madison, USA; (2013) Fse - Ferrovie del Sud Est E Servizi Automobilistici, , http://www.fseonline.it, FSE - Ferrovie del Sud Est; Ismail, S., Railway traffic control and train scheduling based on inter-train conflict management (1999) Transports Research, Part B, 33, pp. 511-534; Vicuna, G., (1989) Organizzazione E Tencica Ferroviaria, 2. , CIFI; Collegio Ingegneri Ferroviari Italiani, Roma}, document_type={Conference Paper}, source={Scopus}, }`

- Dotoli, M., Epicoco, N., Falagario, M., Sciancalepore, F. & Costantino, N. (2014) A Nash equilibrium simulation model for the competitiveness evaluation of the auction based day ahead electricity market. IN Computers in Industry, 65.774-785.

[Bibtex]`@ARTICLE{Dotoli2014774, author={Dotoli, M. and Epicoco, N. and Falagario, M. and Sciancalepore, F. and Costantino, N.}, title={A Nash equilibrium simulation model for the competitiveness evaluation of the auction based day ahead electricity market}, journal={Computers in Industry}, year={2014}, volume={65}, number={4}, pages={774-785}, doi={10.1016/j.compind.2014.02.014}, note={cited By 14}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84897413926&doi=10.1016%2fj.compind.2014.02.014&partnerID=40&md5=d05586a0a1e5f5199249321a07505bc7}, abstract={This paper presents a simulation model based on the Nash equilibrium notion for the auction based day ahead electricity generation market. The presented model enhances a previous formalism proposed in the related literature by employing empirical data distributions of the market clearing price as registered by the market authority (e.g. the Independent System Operator). The model is effective when power suppliers with different generation capacities are considered, differently from the starting model that unrealistically assumes equal capacities. The proposed approach aims at evaluating the electricity market competitiveness with regard to the bidder strategies in order to prevent their anticompetitive actions. The framework is applied to a real data set regarding the Italian electricity market to enlighten its effectiveness in different scenarios, varying the number and capacity of participating bidders. The model can be employed as a basis for a decision support tool both for market participants (to define their optimal bidding strategy) and regulators (to avoid collusive strategies). © 2014 Elsevier B.V.}, author_keywords={Competitiveness; Electricity generation market; Market dynamics; Modelling; Nash equilibrium; Simulation}, keywords={Competition; Computation theory; Decision support systems; Electric industry; Electric power generation; Game theory; Models, Competitiveness; Electricity generation; Market dynamics; Nash equilibria; Simulation, Power markets}, references={Al-Agtash, S., Electricity agents in smart grid markets (2013) Computers in Industry, 64 (3), pp. 235-241; Al-Agtash, S., Al-Fayoumi, N., A trade server for electricity e-commerce (2002) Computers in Industry, 47, pp. 89-97; Argotte, L., Mejia-Lavalle, M., Sosa, R., Business intelligence and energy markets: A survey (2009) International Conference on Intelligent System Applications to Power Systems (ISAP'09), pp. 1-6. , Curitiba, Brazil; Attaviriyanupap, P., Kita, H., Tanaka, E., Hasegawa, J., New bidding strategy formulation for day ahead energy and reserve markets based on evolutionary programming (2005) Electrical Power & Energy Systems, 27, pp. 157-167; Berry, C.A., Hobbs, B.F., Meroney, W.A., O'Neill, R.P., Steward, W.R., Analyzing strategic bidding behavior in transmission networks (1999) Game Theory Applications in Electric Power Markets, p. 1999. , IEEE PES Winter Meeting New York; Bevilacqua, V., Costantino, N., Dotoli, M., Falagario, M., Sciancalepore, F., Strategic design and multi-objective optimization of distribution networks based on genetic algorithms (2012) International Journal of Computer Integrated Manufacturing, 25 (12), pp. 1139-1150; Borenstein, S., Bushnell, S., Kahn, E., Stoft, S., Market power in California electricity markets (1995) Utilities Policy, 5 (3), pp. 219-236; Cardell, J., Hitt, C.C., Hogan, W.W., Market power and strategic interaction in electricity networks (1997) Resource and Energy Economics, 19 (1-2), pp. 109-137; Christie, R.D., Wollenberg, B.F., Wangensteen, L., Transmission management in the deregulated environment (2000) Proceedings of the IEEE, 88 (2), pp. 170-195; Costantino, N., Dotoli, M., Falagario, M., Fanti, M.P., Iacobellis, G., A decision support system framework for purchasing management in supply chains (2009) Journal of Business & Industrial Marketing, 24 (3-4), pp. 278-290; Costantino, N., Dotoli, M., Falagario, M., Fanti, M.P., Mangini, A.M., Sciancalepore, F., Ukovich, W., A fuzzy programming approach for the strategic design of distribution networks (2011) 2011 IEEE International Conferance on Automation Science and Engineering (CASE 2011), , Trieste (Italy), August 24-27; Costantino, N., Dotoli, M., Falagario, M., Sciancalepore, F., Balancing the additional costs of purchasing and the vendor set dimension to reduce public procurement costs (2012) Journal of Purchasing and Supply Management, 18 (3), pp. 189-198; Costantino, N., Dotoli, M., Falagario, M., Fanti, M.P., Mangini, A.M., A model for supply management of agile manufacturing supply chains (2012) International Journal of Production Economics, 135 (1), pp. 451-457; David, A.K., Competitive bidding in electricity supply (1993) IEE Proceedings of the Generation Transmission and Distribution, 140 (5), pp. 421-426; David, A.K., Wen, F., Strategic bidding in competitive electricity market: A literature survey (2000) Proceedings of the IEEE Power Engineering Society Power Engineering Conference, pp. 2168-2173; De La Torre, S., Contreras, J., Conejo, A.J., Finding multiperiod Nash equilibria in pool-based electricity markets (2004) IEEE Transactions on Power Systems, 19 (1), pp. 643-651; Dotoli, M., Falagario, M., A hierarchical model for optimal supplier selection in multiple sourcing contexts (2012) International Journal of Production Research, 50 (11), pp. 2953-2967; Faria, E., Fleten, S.-E., Day-ahead market bidding for a Nordic hydropower producer: Taking the Elbas market into account (2009) Computational Management Science, 8, pp. 75-101; Ferrero, R.W., Rivera, J.F., Price-taker bidding strategy under price uncertainty (2002) IEEE Transactions on Power Systems, 17, pp. 1081-1088; Ferrero, R.W., Shahidehpour, S.M., Ramesh, V.C., Transaction analysis in deregulated power systems using game theory (1997) IEEE Transactions on Power Systems, 12 (3), pp. 1340-1347; Fleten, S.E., Ziemba, W.T., Wallace, S.W., Hedging electricity portfolios via stochastic programming (2002) Decision Making under Uncertainty: Energy and Power 128 of IMA Volumes on Mathematics and Its Applications, pp. 71-93. , C. Greengard, A. Ruszczynski, Springer New York; Gme, (2012) Statistics and Monitoring, , http://www.mercatoelettrico.org/, available at; Grigori, D., Casati, F., Castellanos, M., Daya, U., Sayal, M., Shan, M.C., Business process intelligence (2004) Computers in Industry, 53 (3), pp. 321-343; Guan, X., Ho, Y.C., Lai, F., An ordinal optimization based bidding strategy (2001) IEEE Transactions on Power Systems, 16, pp. 788-797; Hao, S., A study of basic bidding strategy in clearing pricing auctions (2000) IEEE Transactions on Power Systems, 15, pp. 975-980; Hirschman, A.O., The paternity of an index (1964) The American Economic Review, 54 (5), p. 761; Hobbs, B.F., Metzler, C.B., Pang, J., Strategic gaming analysis for electric power systems: An MPEC approach (2000) IEEE Transactions on Power Systems, 15 (2), pp. 638-645; Hobbs, B.F., Schuler, R.E., Assessment of the deregulation of electric power generation using network models of imperfect spatial competition (1985) Papers in Regional Science, 57, pp. 75-89; Irastorza, V., Fraser, H., Are ITP-run day-ahead markets needed? (2002) Electricity Journal, 15 (9), pp. 25-33; Klemperer, P.D., Meyer, M.A., Supply function equilibrium in oligopoly under uncertainty (1989) Econometrica, 57 (6), pp. 1243-1277; Kwon, R.H., Frances, D., Optimization-based bidding in day-ahead electricity auction markets: A review of models for power producers. Handbook of networks in power systems (2012) Energy Systems, 2012, pp. 41-59; Leeprechanon, N., David, A.K., Moorthy, S.S., Brooks, R.D., Nealand, J.H., Market power in developing countries (2002) International Conference on Power System Technology, Vol. 3, pp. 1805-1813. , Kunming, China; Li, T., Shahidehpour, M., Strategic bidding of transmission-constrained Gen-Cos with incomplete information (2005) IEEE Transactions on Power Systems, 20, pp. 437-447; Li, G., Shi, J., Qu, X., Modeling methods for GenCo bidding strategy optimization in the liberalized electricity spot market: A state-of-the-art review (2011) Energy, 36 (8), pp. 4686-4700; Ma, X.W., Sun, D.I., Cheung, K.W., Evolution toward standardized market design (2003) IEEE Transactions on Power Systems, 18, pp. 460-469; Mahvi, M., Ardehali, M.M., Optimal bidding strategy in a competitive electricity market based on agent-based approach and numerical sensitivity analysis (2011) Energy, 36, pp. 6367-6374; McAfee, R.P., McMillan, J., Auctions and bidding (1987) Journal of Economic Literature, 25 (2), pp. 699-738; McGovern, T., Hicks, C., Deregulation and restructuring of the global electricity supply industry and its impact upon power plant suppliers (2004) International Journal of Production Economics, 89, pp. 321-337; Nanduri, V., Das, T.K., A survey of critical research areas in the energy segment of restructured electric power markets (2009) Electrical Power and Energy Systems, 31, pp. 181-191; Ni, E., Luh, P.B., Rourke, S.O., Optimal integrated generation bidding and scheduling with risk management under a deregulated power market (2004) IEEE Transactions on Power Systems, 19 (1), pp. 600-609; Nogueira, T., Vale, A., Vale, Z., An electricity day-ahead market simulation model (2003) International Conference on Renewable Energy and Power Quality (ICREPQ'03), p. 5; Park, J.B., Kim, B.H., Kim, J.H., Jung, M.H., Park, J.K., A continuous strategy game for power transactions analysis in competitive electricity markets (2001) IEEE Transactions on Power Systems, 16 (4), pp. 847-855; Rajamaran, R., Kirsch, L., Alvarado, F., Clark, C., Optimal selfcommitment under uncertain energy and reserve prices (2001) The Next Generation of Electric Power Unit Commitment Models, International Series in Operations Research & Management Science, pp. 93-116. , B.F. Hobbs, M.H. Rothkop, R.P. O'Neill, H.-P. Chao, Kluwer Academic Publ. Boston; Sheffrin, A., Predicting market power using the residual supply index (2002) FERC Market Monitoring Workshop, December 3-4, p. 2002; Shreshta, G.B., Goel, L.K.S., Strategic bidding for minimum power output in the competitive power market (2001) IEEE Transactions on Power Systems, 16, pp. 813-819; Song, H., Liu, C.C., Lawarreé, J., Dahlgren, R.W., Optimal electricity supply bidding by Markov decision process (2000) IEEE Transactions on Power Systems, 15 (2), pp. 618-624; Ventosa, M., Baíllo, Á., Ramos, A., Rivier, M., Electricity market modeling trends (2005) Energy Policy, 33 (7), pp. 897-913; Vickrey, W.S., Counterspeculation, auctions, and competitive sealed tenders (1961) Journal of Finance, 16 (1), pp. 8-37; Wei, J.Y., Smeers, Y., Spatial oligopolistic electricity models with Cournot generators and regulated transmission prices (1999) Operations Research, 47 (1), pp. 102-112; Wen, F., David, A.K., Optimal bidding strategies and modeling of imperfect information among competitive generators (2001) IEEE Transactions on Power Systems, 16 (1), pp. 15-21; You, W.X., Xu, D.D., Li, W.W., Research on optimal bidding strategies for power suppliers based on sealed auction (2010) International Conference on Power System Technology, pp. 1-5. , Hangzhou, China; Yu, Z., Sparrow, F.T., Morin, T.L., Nderitu, G., A Stackelberg price leadership model with application to deregulated electricity markets (2000) Proceedings of IEEE Power Engineering Society, , Winter Meeting, Singapore, January}, document_type={Article}, source={Scopus}, }`

### 2013

- Dassisti, M., Dotoli, M. & Chen, D. (2013) Interoperability analysis: General concepts for an axiomatic approach IN IEEE International Conference on Emerging Technologies and Factory Automation, ETFA..

[Bibtex]`@CONFERENCE{Dassisti2013, author={Dassisti, M. and Dotoli, M. and Chen, D.}, title={Interoperability analysis: General concepts for an axiomatic approach}, journal={IEEE International Conference on Emerging Technologies and Factory Automation, ETFA}, year={2013}, doi={10.1109/ETFA.2013.6648169}, art_number={6648169}, note={cited By 0}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84890751533&doi=10.1109%2fETFA.2013.6648169&partnerID=40&md5=d78c6370027430a4085515afb0068181}, abstract={The paper provides general criteria and evidences for the design phase of an interoperable enterprise system. The analysis of interoperability is introduced, to characterize features and criticalities for the subsequent design actions to be undertaken. An axiomatic approach is proposed to this aim, providing general principles to be followed. A simple case study is discussed. © 2013 IEEE.}, keywords={Axiomatic approach; Design phase; Enterprise system, Factory automation, Interoperability}, references={Von Bertalanffy, L., (1974) Perspectives on General SystemTheory, , New York; Chen, D.D., Doumeingts, G., Decisional systemsdesign using GRAI method (1992) Proc. IA'92 Int. Conf. Ind. Autom., pp. 20-23. , May. Singapore; Costantino, N., Dotoli, M., Falagario, M., Fanti, M.P., Mangini, A.M., A model for supply management of agilemanufacturing supply chains (2012) Int. J. Prod. Econ, 135 (1), pp. 451-457; Dassisti, M., Formal modelling approaches for integratedquality-management systems in delocalised enterprises: Ataxonomy of requirements (2003) Int. J. Autom. Techn. Man., 3 (34); Dassisti, M., Scorziello, F., Enterprise modelinginteroperability: A case study of two university laboratories (2004) Proc. CIRP ICME '04, pp. 61-65. , 30 June-2 July, Sorrento; Doumeingts, G., Vallespir, B., Chen, D., (1998) Decisionmodelling GRAI Grid. Handbook on Architectures OfInformation Systems, pp. 313-337. , Eds. : P. Bernus K. Mertins, G. Schmidt, Springer; Forrester, J.W., (1968) Principles of Systems, , 2nd ed. ). Waltham. MA: Pegasus Communications; IEEE Computer Society, IEEE Standard Glossary ofSoftware Engineering Terminology, 1990; Mertins, K., Jochem, R., (1998) MO2GO. in P. Bernus, K. Mertins and G. Schmidt, Handbook on Architectures OfInformation Systems. Springer-Verlag, pp. 589-600; Mesarovic, M.D., MacKo, D., Takahara, Y., (1970) Theory Ofhierarchical, Multilevel, Systems, , Academic Press, NewYork and London; Mintzberg, H., (1979) The Structure of Organisations: A Synthesisof Research, , Pretince-Hall; Simon, E., (1969) The Science of the Artificial, , MIT Press. Cambridge. MA; Suh, N.P., (1990) The Principles of Design, , Edition OxfordUniversity Press; Vernadat, F., (1996) Enterprise Modelling and Integration: Principles and Applications, , Chapman&Hall, London; Vernadat, F., Enterprise Modelling andIntegration-From fact modelling to EnterpriseInteroperability (2003) Enterprise Inter-and-Intraorganisational Integration, Building Internationalconsensus, , eds. Kurt Kosanke et al. ), Kluwer}, document_type={Conference Paper}, source={Scopus}, }`

- Dotoli, M., Epicoco, N., Falagario, M. & Costantino, N. (2013) A lean warehousing integrated approach: A case study IN IEEE International Conference on Emerging Technologies and Factory Automation, ETFA..

[Bibtex]`@CONFERENCE{Dotoli2013, author={Dotoli, M. and Epicoco, N. and Falagario, M. and Costantino, N.}, title={A lean warehousing integrated approach: A case study}, journal={IEEE International Conference on Emerging Technologies and Factory Automation, ETFA}, year={2013}, doi={10.1109/ETFA.2013.6648030}, art_number={6648030}, note={cited By 7}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84890747024&doi=10.1109%2fETFA.2013.6648030&partnerID=40&md5=f13085b97bcc615f313821862675c0ce}, abstract={This paper focuses on reengineering of production warehouses with lean manufacturing. Using as a case study an Italian interior design producer, we present an integrated approach for lean warehousing. Firstly a detailed description of the warehouse logistics is provided by the Unified Modeling Language (UML), hence Value Stream Mapping (VSM) allows identifying non-value adding activities, and the Gemba Shikumi technique helps to rank such anomalies. The reapplication of VSM produces an overall picture of the optimized warehouse, and using UML we detail the reengineered warehouse processes. The approach represents a useful tool to systematically improve production warehouse management. © 2013 IEEE.}, keywords={Integrated approach; Interior designs; Lean manufacturing; Lean warehousing; Value stream mapping; Warehouse management, Architectural design; Factory automation; Integrated control; Unified Modeling Language, Warehouses}, references={Abdulmalek, A., Rajgopal, J., Analyzing the benefits of lean manufacturing and value stream mapping via simulafion: A process sector case study (2007) Int. J. Prod. Econ., 107 (1), pp. 223-236; Aichlmayr, M., Training ensures wms results (2002) Transp. &Di. Ctrib., 43 (10), pp. 52-57; Baker, P., Canessa, M., Warehouse design: A structured approach (2009) European. Iournal of Operational Re. Cearch, 193, pp. 425-436; Braglia, M., Carmignani, G., Zammori, F., A newvalue stream mapping approach for complex productionsystem (2006) Int. J. Prod. Res., 44, pp. 3929-3952; Bevilacqua, V., Costantino, N., Dotoli, M., Falagario, M., Sciancalepore, F., Strategic design and multi-objectiveoptimization of distribution networks based on geneticalgorithms Int. J. Com. Int. Manuf, 25 (12), pp. 1139-1150; Costantino, N., Dotoli, M., Falagario, M., Fanti, M.P., Mangini, A.M., A model for supply management of agilemanufacturing supply chains (2012) Int. J. Prod. Econ., 135, pp. 451-457; Costantino, N., Dotoli, M., Falagario, M., Fanti, M.P., Mangini, A.M., Sciancalepore, F., A fuzzy programmingapproach for the strategic design of distributionnetworks (2011) CASE 2011, Trieste (Italy, 8, pp. 24-27; Dassisti, M., Dotoli, M., Epicoco, N., Falagario, M., Internal logistics integration by automated storage andretrieval systems: A reengineering case study (2012) 7thInt. Worksh. on Enterprise Integration, Interoperabilityand Networking, Rome, Italy, pp. 78-82; De Koster, R., Le-Duc, T., Roodbergen, K.J., Designand control of warehouse order picking: A literaturereview (2007) Eur. J. Op. Res., 182, pp. 481-501; Dharmapriya, U.S.S., Kulatunga, A.K., New strategyfor warehouse optimization-lean warehousing (2011) 2011Int. Conf. Ind. Eng. Op. Man., , Kuala Lumpur, Malaysia, January 22-24; Dotoli, M., Fanti, M.P., A coloured timed Petri netmodel for automated storage and retrieval systemsserviced by rail-guided vehicles: A control perspective (2005) Int. J. Comp. Int. Manuf., 18, pp. 122-136; Dotoli, M., Fanti, M.P., Deadlock detection andavoidance strategies for automated storage and retrievalsystems (2007) IEEE Trans. Sys. Man Cyb., Part C, 37, pp. 541-552; Gu, J.X., Goetschalckx, M., McGinnis, L.F., Researchon warehouse design and performance evaluation: Acomprehensive review (2010) Eur. J. Op. Res., 203 (3), pp. 539-549; Pan, G.-Q., Feng, D.-Z., Jiang, M.-X., ApplicationResearch of shortening delivery time through valuestream mapping analysis (2010) IEEE 17th Int. Conf. Ind. Eng. Eng. Man., pp. 733-736; Ramesh, V., Prasad, S.K.V., Srinivas, T.R., Implementation of a lean model for carrying out valuestream mapping in a manufacturing industry (2008) J. Ind. Sys. Eng., 2, pp. 180-196; Rother, M., Shook, J., Learning to see: Value streammapping to add value and eliminate muda (1999) The LeanEnterprise Institute, , Inc., Brookline, MA; Rouwenhorst, B., Reuter, B., Stockrahm, V., Vanhoutum, G.J., Mantel, R.J., Zijm, W.H.M., Warehousedesign and control: Framework and literature review (2000) Eur. J. Op. Res., 122, pp. 515-533; Scott, N.A., Lean conversion and genba shikumi (2007) Int. Conf. Ag. Manuf., pp. 168-171; Seth, D., Gupta, V., Application of value streammapping for lean operations and cycle time reduction: AnIndian case study (2005) Prod. Plan. Ctrl., 16 (1), pp. 44-59; Shiau, J.-Y., Lee, M.C., A warehouse managementsystem with sequential picking for multi-containerdeliveries (2010) Comp. Ind. Eng., 58, pp. 382-392; Tompkins, J.A., Smith, J.D., (1998) The WarehouseManagement Handbook, , 2nd Edition, Tompkins Pres, Raleigh, NC, USA; Womack, P., Jones, D.T., Lean thinking (1996) Simon AndSchuster, , New York; Van Den Berg, J.P., A literature survey on planning andcontrol of warehousing systems (1999) IIE Trans., 31, pp. 751-762; Van Den Berg, J.P., Zijm, W.H.M., Models forwarehouse management: Classification and examples (1999) Int. J. Prod. Econ., 59, pp. 519-528}, document_type={Conference Paper}, source={Scopus}, }`

- Dotoli, M., Epicoco, N., Falagario, M., Palma, D. & Turchiano, B. (2013) A train load planning optimization model for intermodal freight transport terminals: A case study IN Proceedings – 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013., 3597-3602.

[Bibtex]`@CONFERENCE{Dotoli20133597, author={Dotoli, M. and Epicoco, N. and Falagario, M. and Palma, D. and Turchiano, B.}, title={A train load planning optimization model for intermodal freight transport terminals: A case study}, journal={Proceedings - 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013}, year={2013}, pages={3597-3602}, doi={10.1109/SMC.2013.613}, art_number={6722366}, note={cited By 7}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84893630792&doi=10.1109%2fSMC.2013.613&partnerID=40&md5=6f33f8006bb4631e581bf192e55b9724}, abstract={Despite the emerging positive trend of rail freight transport, especially in intermodal contexts, the optimization of intermodal terminals is addressed only by few studies and mainly for seaport terminals. This paper fills this gap presenting a train load1 planning optimization model for intermodal rail-road terminals. The proposed model maximizes the train commercial value while respecting priority, physical, financial, and prosecution constraints (i.e., taking into account containers that prosecute to a subsequent terminal after the train destination). The presented method has been successfully tested on a real case study - The rail-road terminal of an Italian intermodal logistics company that is a leader in the European market - showing its effectiveness and ease of application. © 2013 IEEE.}, keywords={European markets; Intermodal freight transport; Intermodal terminals; Logistics company; Optimization modeling; Rail freights; Rail-road terminals; Real case, Cybernetics; Mathematical models; Roads and streets, Freight transportation}, references={Abacoumkin, C., Ballis, A., Development of an expert system for the evaluation of conventional and innovative technologies in the intermodal transport area (2004) European Journal of Operational Research, 152, pp. 410-419; Ambrosino, D., Bramardi, A., Pucciano, M., Sacone, S., Siri, S., Modeling and solving the train load planning problem in seaport container terminals (2011) 7th Conference on Automation Science and Engineering, pp. 208-213; Arnold, P., Peeters, D., Thomas, I., Modelling a rail/road intermodal transportation system (2004) Transportation Research E, 40, pp. 255-270; Baccelli, O., (2001) La Mobilità delle Merci in Europa. Potenzialità del Trasporto Intermodale, , EGEA, Milan, Italy, (in Italian; Ballis, A., Golias, J., Towards the improvement of a combined transport chain performance (2004) European Journal of Operational Research, 152, pp. 420-436; Bish, E., A multiple-crane-constrained scheduling problem in a container terminal (2003) European Journal of Operational Research, 144, pp. 83-107; Boardman, B., Trusty, K., Malstrom, E., (1999) Intermodal Transportation Cost Analysis Tables, , Department of Industrial Engineering, University of Arkansas; Boschian, V., Dotoli, M., Fanti, M.P., Iacobellis, G., Ukovich, W., A metamodelling approach to the management of intermodal transportation networks (2011) IEEE Transactions on Automation Science and Engineering, 8 (3), pp. 457-469; Bostel, N., Dejax, P., Models and algorithms for container allocation problems on trains in a rapid transshipment shunting yard (1998) Transportation Science, 32 (4), pp. 370-379; Bruns, F., Knust, S., Optimized load planning of trains in intermodal transportation (2012) Or Spectrum, 34 (3), pp. 511-533; Caris, A., Macharis, C., Jansses, G.K., Planning problems in intermodal freight transport: Accomplishments and prospects (2008) Transportation Planning and Technology, 31 (3), pp. 277-302; Corry, P., Kozan, E., An assignment model for dynamic load planning of intermodal trains (2006) Computers & Operations Research, 33, pp. 1-17; Costantino, N., Dotoli, M., Falagario, M., Fanti, M.P., Mangini, A.M., A model for supply management of agile manufacturing supply chains (2012) International Journal of Production Economics, 135 (1), pp. 451-457; Dotoli, M., Sciancalepore, F., Epicoco, N., Falagario, M., Turchiano, B., Costantino, N., A periodic event scheduling approach for offline timetable optimization of regional railways (2013) Proc. 10th IEEE International Conference on Networking, Sensing and Control (ICNSC2013), , Paris, France, April 10-12; Dotoli, M., Fanti, M.P., Mangini, A.M., Stecco, G., Ukovich, W., The impact of ICT on intermodal transportation systems: A modelling approach by petri nets (2010) Control Engineering Practice, 18 (8), pp. 893-903. , August; Ferreira, L., Sigut, J., Modelling intermodal freight terminal operations (1995) Road and Transportation Research, 4 (4), pp. 4-16; Geng, G., Li, L., Scheduling railway freight cars (2001) Knowledge- Based Systems, 14, pp. 289-297; Gnu Linear Programming, , http://www.gnu.org/software/glpk/; Gonzalez, J.A., Ponce, E., Mataix, C., Carrasco, J., The automatic generation of transhipment plans for a train-train terminal: Application to the spanish-french border (2008) Transportation Planning and Technology, 31 (5), pp. 545-567; Kim, K., Evaluation of the number of rehandles in container yards (1997) Computers in Industrial Engineering, 32 (4), pp. 701-711; Martínez, F.M., Gutiérrez, I.G., Oliveira, A.O., Bedia, L.M.A., Gantry crane operations to transfer containers between trains: A simulation study of a spanish terminal (2003) Transportation Planning and Technology, 27, pp. 261-284; Mattfeld, D., Kopfer, H., Terminal operations management in vehicle transshipment (2003) Transportation Research, Part a, 37, pp. 435-452; Stahlbock, R., Voss, S., Operations research at container terminals: A literature update (2008) Or Spectrum, 30, pp. 1-52; Terminology on combined transport (2001) Economic Commission for Europe (UN/ECE), the European Conference of Ministers of Transport (ECMT) and the European Commission (EC), , United Nations, ", ", Prepared by the, New York and Geneva}, document_type={Conference Paper}, source={Scopus}, }`

- Carli, R., Dotoli, M., Pellegrino, R. & Ranieri, L. (2013) Measuring and managing the smartness of cities: A framework for classifying performance indicators IN Proceedings – 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013., 1288-1293.

[Bibtex]`@CONFERENCE{Carli20131288, author={Carli, R. and Dotoli, M. and Pellegrino, R. and Ranieri, L.}, title={Measuring and managing the smartness of cities: A framework for classifying performance indicators}, journal={Proceedings - 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013}, year={2013}, pages={1288-1293}, doi={10.1109/SMC.2013.223}, art_number={6721976}, note={cited By 62}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84893629343&doi=10.1109%2fSMC.2013.223&partnerID=40&md5=2e7a1d0751ad2b7391780dc51c8b26b0}, abstract={Due to the continuous increase of the world population living in cities, it is crucial to identify strategic plans and perform associated actions to make cities smarter, i.e., more operationally efficient, socially friendly, and environmentally sustainable, in a cost effective manner. To achieve these goals, emerging smart cities need to be optimally and intelligently measured, monitored, and managed. In this context the paper proposes the development of a framework for classifying performance indicators of a smart city. It is based on two dimensions: The degree of objectivity of observed variables and the level of technological advancement for data collection. The paper shows an application of the presented framework to the case of the Bari municipality (Italy). © 2013 IEEE.}, author_keywords={Information and communication technologies; Management; Monitoring; Smart cities; Smartness indicators}, keywords={Cost effective; Data collection; Information and Communication Technologies; Performance indicators; Smart cities; Strategic plan; Technological advancement; World population, Cybernetics; Electronic commerce; Information technology; Management; Monitoring, Benchmarking}, references={Caragliu, A., Del Bo, C., Nijkamp, P., Smart cities in Europe (2009) Proc. of 3rd Central European Conf. in Regional Science (CERS), , Oct; Harrison, C., Eckman, B., Hamilton, R., Hartswick, P., Kalagnanam, J., Paraszczak, J., Williams, P., Foundations for smarter cities (2010) IBM Journal of Research and Development, 54 (4), pp. 1-16; Chourabi, H., Taewoo, N., Walker, S., Gil-Garcia, J.R., Mellouli, S., Nahon, K., Pardo, T.A., Scholl, H.J., Understanding smart cities: An integrative framework (2012) Proc. System Science (HICSS), 2012 45th Hawaii Int. Conf, pp. 2289-2297. , 4-7 Jan; Batty, M., Axhausen, K.W., Giannotti, F., Pozdnoukhov, A., Bazzani, A., Wachowicz, M., Ouzounis, G., Portugali, Y., Smart cities of the future (2012) The European Physical Journal, 214 (1), pp. 481-518; A Vision of Smarter Cities, , http://www.ibm.com/; (2012) Smart Cities in Italy: An Opportunity in the Spirit of the Renaissance for a New Quality of Life, , http://www.ambrosetti.eu/; Giffinger, R., Fertner, C., Kramar, H., Kalasek, R., Pichler-Milanović, N., Meijers, E., (2007) Smart Cities: Ranking of European Medium- Sized Cities, , http://www.smartcities.eu/, Vienna, Austria, available at; Washburn, D., Sindhu, U., Balaouras, S., Dines, R.A., Hayes, N.M., Nelson, L.E., (2010) Helping CIOs Understand "Smart City" Initiatives, , Cambridge, MA: Forrester Research; Berry, C.R., Glaeser, E.L., The divergence of human capital levels across cities (2005) Regional Science, 84 (3), pp. 407-444. , Papers in; Glaeser, E.L., Berry, C.R., Why are smart places getting smarter? (2006) Taubman Cente Policy Brief 2006-2, , Cambridge MA; Hsich, H.-N., Chen, C.-C., Chou, C.-Y., Chen, Y.-Y., The evaluating indices and promoting strategies of intelligent city in Taiwan (2011) Proc. Multimedia Technology Int. Conf, pp. 6704-6709. , 26-28 July; (2012) La Classifica delle Citta Intelligenti Italiane, , http://www.icitylab.it/il-rapporto-icityrate/cose/, PA Forum, in Italian; (2011) Global Power City Index, , http://www.mori-mfoundation.or.jp/english/research/project/6/pdf/ GPCI2011_English.pdf, Available at; Lazaroiu, G.C., Roscia, M., Definition methodology for the smart cities model (2012) Energy, 47 (1), pp. 326-332; Schomaker, M., Development of environmental indicators in unep (1997) Land Quality Indicators and Use in Sustainable Agriculture and Rural Development, pp. 35-36. , Jan. 25-26, 1996, Rome; Smart Grid/Department of Energy, , http://energy.gov/oe/technology-development/smart-grid; Hancke, G.P., Silva, B.C., Hancke Jr., G.P., The role of advanced sensing in smart cities (2013) Sensors, 13, pp. 393-425; Mitton, N., Papavassiliou, S., Puliafito, A., Trivedi, K.S., Combining cloud and sensors in a smart city environment (2012) EURASIP Journal on Wireless Communications and Networking 2012, p. 247; Foth, M., (2009) Handbook of Research on Urban Informatics: The Practice and Promise of the Real-Time City, , Hershey. Ed; Frank, R., Mouton, M., Engel, T., Towards collaborative traffic sensing using mobile phones (poster) (2012) Vehicular Networking Conference (VNC), 2012 IEEE, pp. 115-120. , 14-16 Nov; Eagle, N., Pentland, A., Reality mining: Sensing complex social systems (2006) Personal Ubiquitous Computing, 10, pp. 255-268; Girardin, F., Calabrese, F., Fiore, F.D., Ratti, C., Blat, J., Digital footprinting: Uncovering tourists with user-generated content (2008) Pervasive Computing, IEEE, 7 (4), pp. 36-43. , Oct.-Dec; Calabrese, F., Colonna, M., Lovisolo, P., Parata, D., Ratti, C., Real-time urban monitoring using cell phones: A case study in rome (2011) IEEE Transactions on Intelligent Transportation Systems, 12 (1), pp. 141-151; IHappy, , http://voicesfromtheblogs.com/category/english/, (in Italian)}, document_type={Conference Paper}, source={Scopus}, }`

- Costantino, N., Dotoli, M., Epicoco, N., Falagario, M. & Sciancalepore, F. (2013) Using cross-efficiency fuzzy Data Envelopment Analysis for healthcare facilities performance evaluation under uncertainty IN Proceedings – 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013., 912-917.

[Bibtex]`@CONFERENCE{Costantino2013912, author={Costantino, N. and Dotoli, M. and Epicoco, N. and Falagario, M. and Sciancalepore, F.}, title={Using cross-efficiency fuzzy Data Envelopment Analysis for healthcare facilities performance evaluation under uncertainty}, journal={Proceedings - 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013}, year={2013}, pages={912-917}, doi={10.1109/SMC.2013.160}, art_number={6721913}, note={cited By 5}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84893537583&doi=10.1109%2fSMC.2013.160&partnerID=40&md5=c8c238d8356ba6d8cbe50e98c573a3c4}, abstract={address the problem of healthcare systems performance evaluation under uncertainty by a cross-efficiency fuzzy Data Envelopment Analysis (DEA) technique. Triangular fuzzy numbers are employed to deal with uncertain data. More precisely, a fuzzy triangular efficiency is associated to each hospital/ward through a cross-evaluation by a compromise between objectives. Results are defuzzified to obtain the ranking. The method is applied to evaluate hospitals in a region of Southern Italy and estimate the temporal evolution of the performance of one of them, showing the ease of application and usefulness in validating and planning healthcare reforms. © 2013 IEEE.}, keywords={Cross-efficiency; Fuzzy data envelopment analysis; Health-care system; Healthcare facility; Healthcare reforms; Temporal evolution; Triangular fuzzy numbers; Uncertain datas, Cybernetics; Data envelopment analysis; Efficiency; Fuzzy sets, Health care}, references={Aristovnik, A., Measuring relative efficiency in health and education sector: The case of east european countries (2012) Act. Probl. Econ., 136, pp. 305-314; Barros, C.P., De Menezes, A.G., Vieira, J.C., Measurement of hospital efficiency, using a latent class stochastic frontier model (2013) Appl. Econ., 45, pp. 47-54; Bryce, C.L., Engberg, J.B., Wholey, D.R., Comparing the agreement among alternative models in evaluating hmo efficiency (2000) Heal. Serv. Res., 35, pp. 509-528; Charnes, A., Cooper, W.W., Rhodes, E., Measuring the efficiency of decision making units (1978) Eur. J. Oper. Res., 2, pp. 429-444; Costantino, N., Dotoli, M., Epicoco, N., Falagario, M., Sciancalepore, F., A cross efficiency fuzzy data envelopment analysis technique for supplier evaluation under uncertainty (2012) Proc. ETFA 2012, , Krakòv (Poland), September 17-21; Costantino, N., Dotoli, M., Epicoco, N., Falagario, M., Sciancalepore, F., A novel fuzzy data envelopment analysis methodology for performance evaluation in a two-stage supply chain (2012) Proc. CASE2012, , Seoul (Korea), August 20-24; Costantino, N., Dotoli, M., Falagario, M., Fanti, M.P., Mangini, A.M., Sciancalepore, F., Ukovich, W., A model for the optimal design of the hospital drug distribution system (2010) Proc. IEEE WHCM2010, p. 6. , Workshop, Venice, Italy, 18-20 February; Costantino, N., Dotoli, M., Falagario, M., Fanti, M.P., Mangini, A.M., Sciancalepore, F., Ukovich, W., Optimal design of distribution networks: An application to the hospital drug distribution system (2010) Proc. ORAHS2010, , Genoa, Italy, July 18- 23; Fanti, M.P., Mangini, A.M., Dotoli, M., Ukovich, W., A three level strategy for the design and performance evaluation of hospital departments (2012) IEEE Trans. Sys. Man Cyb. Part A; Fetter, R.B., Shin, Y., Freeman, J.L., Averill, R.F., Thompson, J.D., Case mix definition by diagnosis-related groups (1980) Med. Car., 18, pp. 1-53; Hollingsworth, B., Dawson, P.J., Maniadakis, N., Efficiency measurement of health care: A review of non-parametric methods and applications (1999) Heal. C. Manag. Sci., 2, pp. 161-172; Hu, H.H., Qi, Q., Yang, C.H., Analysis of hospital technical efficiency in china: Effect of health insurance reform (2012) Ch. Econ. Rev., 23, pp. 865-877; Jimenez, M., Bilbao, A., Pareto-optimal solution in fuzzy multi-objective linear programming (2009) Fuz. Set. Sys., 160, pp. 2714-2721; Liang, T.F., Application of fuzzy sets to manufacturing/distribution planning decisions in supply chains (2011) Inf. Sci., 181, pp. 842-854; Mukherjee, K., Santerre, R.E., Zhang, N.J., Explaining the efficiency of local health departments in the u.s.: An exploratory analysis (2010) Heal. C. Manag. Sci., 13, pp. 378-387; Sexton, T.R., Silkman, R.H., Hogan, A.J., Data envelopment analysis: Critique and extensions (1986) Measuring Efficiency: An Assessment of Data Envelopment Analysis, , Silkman, R.H. (Ed.), Jossey-Bass, San Francisco, CA; Sherman, H.D., Hospital efficiency measurement and evaluation, Empirical test of a new technique (1984) Med. Care, 22 (10), pp. 922-938; Zimmermann, H.J., (2001) Fuzzy Set Theory and its Applications, , 4th Ed, Kluwer Academic Publishers, Boston/Dordrecht/London}, document_type={Conference Paper}, source={Scopus}, }`

- Dotoli, M., Epicoco, N., Falagario, M., Piconese, A., Sciancalepore, F. & Turchiano, B. (2013) A real time traffic management model for regional railway networks under disturbances IN IEEE International Conference on Automation Science and Engineering., 892-897.

[Bibtex]`@CONFERENCE{Dotoli2013892, author={Dotoli, M. and Epicoco, N. and Falagario, M. and Piconese, A. and Sciancalepore, F. and Turchiano, B.}, title={A real time traffic management model for regional railway networks under disturbances}, journal={IEEE International Conference on Automation Science and Engineering}, year={2013}, pages={892-897}, doi={10.1109/CoASE.2013.6653977}, art_number={6653977}, note={cited By 11}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84891508996&doi=10.1109%2fCoASE.2013.6653977&partnerID=40&md5=10f312c662a1b1efe1bca6fbc2462d33}, abstract={We address real time traffic management under disturbances of regional rails with a centralized traffic control system. We solve the rescheduling problem by revisiting a finite time horizon mixed integer linear programming model from the related literature. First, we adapt the framework to regional networks, in which stations are close and the network is mainly constituted by single tracks; second, to solve train conflicts that may occur in the rescheduled timetable after the chosen time horizon, we enhance the model by an iterative heuristic algorithm that solves such conflicts. The presented approach is applied to a real data set related to a large portion of a regional network in Southern Italy, showing its effectiveness in providing a physically realizable rescheduled solution in a very short computational time. © 2013 IEEE.}, author_keywords={events; optimization; Railways; real time conflict resolution; regional networks; train scheduling; train timetable}, keywords={events; Railways; Real time conflict resolution; Regional networks; Train scheduling; Train timetables, Embedded systems; Heuristic algorithms; Iterative methods; Linear programming; Optimization; Railroads, Scheduling}, references={Adenso-Diaz, B., Oliva Gonzalez, M., Gonzàlez-Torre, P., On-line timetable rescheduling in regional train services (1999) TransportationResearch, PartB, 33, pp. 378-398; Caprara, A., Monaci, M., Toth, P., Guida, P.L., A Lagrangian heuristic algorithm for a real-world train timetabling problem (2005) Discrete Applied Mathematics, 154 (5), pp. 738-753; Cordeau, J.F., Toth, P., Vigo, D., A survey of optimization models for train routing and scheduling (1998) Transportation Science, 32 (4), pp. 380-420; Dariano, A., (2008) Improving Real-Time Train Dispatching: Models, Algorithms and Applications, , Ph. D thesis, Dep. of Transport and Planning, Fac. of Civil Engineering and Geosciences, DeIft University of Technology; Dariano, A., Pacciarelli, D., Pranzo, M., A branch and bound algorithm for scheduling trains in a railway network (2007) European Journal of Operational Research, 183 (2), pp. 643-657; Dotoli, M., Sciancalepore, F., Epicoco, N., Falagario, M., Turchiano, B., Costantino, N., A periodic event scheduling approach for offline timetable optimization of regional raylways (2013) Proc. ICNSC 2013, Paris (France); http://www.fseonline.it, FSE-Ferrovie del Sud Est e Servizi Automobilistici; http://www.gnu.org/software/glplc/glpk.html, Gnu Linear Programming; Ismail, S., Railway traffic control and train scheduling based on inter-train conflict management (1999) Transp. Res. PartB, 33, pp. 511-534; Mascis, A., Pacciarelli, D., Job shop scheduling with blocking and no-wait constraints (2002) European Journal of Operational Research, 143, pp. 498-517; Mascis, A., Pacciarelli, D., Pranzo, M., Train scheduling in a regional railway network (2001) Preprints 4th Triennial Symposium on Transportation Analysis, pp. 487-492. , Sao Miguel, Portugal; Oliveira, E., Smith, B.M., A job-shop scheduling model for the single-track railway scheduling problem (2000) School of Computing Research Report, , 2000.21, University of Leeds, England; Tö Rnquist, J., Persson, J.A., Train traffic deviation handling using tabu search and simulated annealing (2005) Proc. of the 38th Annual Hawaii International Conference on System Sciences, pp. 1-10; Tö Rnquist, J., Persson, J.A., N-tracked railway traffic rescheduling during disturbances (2007) Transp. Res. Part B, 41, pp. 342-362}, document_type={Conference Paper}, source={Scopus}, }`

- Fanti, M. P., Mangini, A. M., Dotoli, M. & Ukovich, W. (2013) A three-level strategy for the design and performance evaluation of hospital departments. IN IEEE Transactions on Systems, Man, and Cybernetics Part A:Systems and Humans, 43.742-756.

[Bibtex]`@ARTICLE{Fanti2013742, author={Fanti, M.P. and Mangini, A.M. and Dotoli, M. and Ukovich, W.}, title={A three-level strategy for the design and performance evaluation of hospital departments}, journal={IEEE Transactions on Systems, Man, and Cybernetics Part A:Systems and Humans}, year={2013}, volume={43}, number={4}, pages={742-756}, doi={10.1109/TSMCA.2012.2217319}, note={cited By 43}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84887038165&doi=10.1109%2fTSMCA.2012.2217319&partnerID=40&md5=af8a84aa963d24f0c3b58a21a6606eef}, abstract={The efficient management of hospital departments (HDs) has recently become an important issue. Indeed, the increased demand and design for hospital services have saturated the capacity of HD that requires suitable tools for the efficient use of resources and flow of patients, staff, and drugs. This paper proposes a model based on a three-level strategy to design at the tactical level in a concise and effective way the structure, the resources, and the dynamics of a critically congested HD. The design strategy is composed of three basic elements: the modeling module, the optimization module, and the simulation and decision module. The first module employs a UnifiedModeling Language tool and a timed Petri net (PN) model to effectively capture the detailed flow and dynamics of patients, starting from their arrival to the HD until their discharge. The optimization module employs the fluid relaxation to concisely approximate in a continuous PN framework the HD model and optimize suitable performance indices. The simulation module verifies that the optimized parameters allow an effective workflow organization while maximizing the patient flow. In case of inconsistencies due to the fluid approximation between the continuous model used in the design phase by the optimization module and the discrete one used in the subsequent verification phase by the simulation module, the latter module revises the values of some HD model parameters. A real case study on the Emergency Cardiology Department of the General Hospital of Bari (Italy) shows the efficiency and accuracy of the proposed method. © 2013 IEEE.}, author_keywords={Healthcare systems; Modeling; Performance evaluation; Petri nets (PNs); Simulation}, keywords={Efficient managements; Health-care system; Optimization module; Optimized parameter; Performance evaluation; Performance indices; Petri nets (PNs); Simulation; Continuous modeling; Efficient managements; Fluid approximation; Health-care system; Optimization module; Performance evaluation; Petri nets (PNs); Simulation, Design; Hospitals; Models; Optimization; Petri nets; Tools; Hospitals; Models; Petri nets, Computer simulation; Unified Modeling Language}, references={Aggarwal, V., The application of the unified modeling language in object-oriented analysis of healthcare information systems J. Med. Syst., 26 (5), pp. 383-397. , Oct. 2002; Amodio, G., Fanti, M.P., Mangini, A.M., Un modello basato sulle reti di Petri per la gestione e la valutazione delle prestazioni di un dipartimento di cardiologia d'urgenza (2009) Proc. 40' Nat. Congr. Cardiol. ANMCO, , Florence, Italy Jun. 4-7; Bixby, R.E., Solving real-world linear programs: A decade and more of progress Oper. Res., 50 (1), pp. 3-15. , Jan. -Feb.2002; Cassandras, C.G., Lafortune, S., (2008) Introduction to Discrete Event Systems, , 2nd ed. New York: Springer- Verlag; Choi, S.S., Choi, M.K., Song, W.J., Son, S.H., Ubiquitous RFID healthcare systems analysis on PhysioNet grid portal services using Petri nets (2005) 2005 Fifth International Conference on Information, Communications and Signal Processing, 2005, pp. 1254-1258. , 1689256, 2005 Fifth International Conference on Information, Communications and Signal Processing; Connelly, L.G., Bair, A.E., Discrete event simulation of emergency department activity: A platform for system-level operations research Acad. Emerg. Med., 11 (11), pp. 1177-1185. , Nov. 2004; Criswell, M., Hasan, I., Kopach, R., Lambert, S., Lawley, M., Williams, D.M., Trupiano, G., Varadarajan, N., Emergency department divert avoidance using Petri nets (2007) Proc. Syst. Eng. Conf., pp. 1-6. , San Antonio, TX; Dantzig, G.B., (1963) Linear Programming and Extensions., , Princeton NJ: Princeton Univ. Press; Darabi, H., Galanter, W.L., Lin, J.Y.-Y., Buy, U., Sampath, R., Modeling and integration of hospital information systems with Petri nets (2009) Proc. IEEE/INFORMS Int. Conf. SOLI, pp. 190-195; David, R., Alla, H., (2004) Discrete Continuous and Hybrid Petri Nets., , Berlin Germany: Springer- Verlag; Dotoli, M., Fanti, M.P., Iacobellis, G., Mangini, A.M., A first order hybrid Petri net model for supply chain management IEEE Trans. Autom. Sc. Eng., 6 (4), pp. 744-758. , Nov. 2009; Dotoli, M., Fanti, M.P., Mangini, A.M., Ukovich, W., A continuous Petri net model for the management and design of emergency cardiology departments Proc. 3rd IFAC Conf. ADHS, , Zaragoza, Spain Sep. 16-18 2009; Fanti, M.P., Iacobellis, G., Mangini, A.M., Ukovich, W., A three level strategy for the design and performance evaluation of hospital departments: A case study Proc. 8th IEEE Int. Conf. Autom. Sci. Eng., , Seoul, Korea, Aug. 20-24 2012; Fanti, M.P., Modelli per la valutazione ed il miglioramento della gestione dei sistemi sanitari Proc. Nat. Workshop Errori Medicina Bari, , Italy, Jun. 10-12 2010; Fruggiero, F., Lambiase, A., Vitale, S., Fallon, D., Computer simulation in health care service: The emergency department of CUH (2007) Proc. Int. Conf. Manuf. Res., 3 (2), pp. 142-161; Fruggiero, F., Lambiase, A., Fallon, D., Computer simulation and swarm intelligence organization into an emergency department: A balancing approach across ant colony optimization Int. J. Services Oper. Informat., 3 (2), pp. 142-161. , Jul. 2008; Gosavi, A., (2003) Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning., , Boston MA: Kluwer; Gunal, M., Pidd, M., Interconnected des models of emergency, outpatient and inpatient departments of a hospital (2007) Proc. Winter Simul. Conf., pp. 1461-1465; Gunal, M., Pidd, M., Discrete event simulation for performance modelling in healthcare: A review of the literature (2010) J. Simul., 4 (1), pp. 42-51; Hanna, V., Sethuraman, K., The diffusion of operations management concepts into the healthcare sector (2005) Proc. 3rd ANZAM Oper. Manage. Symp., pp. 132-140; Harper, P.R., A framework for operational modelling of hospital resources Health Care Manage. Sci., 5 (3), pp. 165-173. , Aug. 2002; Hughes, M., Carson, E.R., Makhlouf, M., Morgan, C.J., Summers, R., A Petri net based model of patient-flows in a progressive patient-care system (1998) Proc. IEEE Annu. Int. Conf. Eng. Med. Biol. Soc., pp. 3048-3051; Iannone, R., Pepe, C., Riemma, S., A proposal of a management framework to optimize waiting queue in healthcare organizations (2007) Proc. Int. Conf. Health Care Technol. Manage. Conf., pp. 1-17; Jansen-Vullers, M.H., Reijers, H.A., Business process redesign at a mental healthcare institute: A coloured Petri net approach (2005) Proc. 6th Workshop Pract. Use Coloured Petri Nets CPN Tools, 576, pp. 21-38. , DAIMI, K. Jensen, Ed; Kolker, A., Queuing analytic theory and discrete events simulation for healthcare: Right application for the right problem (2009) Proc. Soc. Health Syst. News, pp. 1-20; Komashie, A., Mousavi, A., Gore, J., Using discrete event simulation (DES) to manage theatre operations in healthcare: An audit-based case study (2008) Proc. Int. Conf. Comput. Model. Simul., pp. 360-365; Konrad, R., Lawley, M., Criswell, M., Incorporating diagnosis based patient flow paths from health information systems messages into hospital decision models (2008) Proc. 3th INFORMS Workshop Data Mining Health Informat., pp. 1-6; Kotb, Y.T., Baumgart, A.S., An extended petri net for modeling workflow with critical sections (2005) Proceedings - ICEBE 2005: IEEE International Conference on e-Business Engineering, 2005, pp. 134-141. , DOI 10.1109/ICEBE.2005.27, 1552882, Proceedings - ICEBE 2005: IEEE International Conference on e-Business Engineering; Kumar, A., Shim, S.J., Eliminating emergency department wait by BPR implementation (2007) IEEM 2007: 2007 IEEE International Conference on Industrial Engineering and Engineering Management, pp. 1679-1683. , DOI 10.1109/IEEM.2007.4419478, 4419478, IEEM 2007: 2007 IEEE International Conference on Industrial Engineering and Engineering Management; Law, A.M., (2007) Simulation Modeling and Analysis, , 2nd ed. New York: McGraw-Hill; Li, J.S., Howard, P.K., Modeling and analysis of hospital emergency department: An analytical framework and problem formulation Proc. 6th IEEE CASE, pp. 897-902. , Toronto, ON, Canada, Aug. 21- 24 2010; Mehrotra, S., On the implementation of a primal-dual interior point method (1992) SIAM J. Optim., 2, pp. 575-601; Miles, R., Hamilton, K., (2006) Learning UML 2.0. Sebastopol, , CA: O'Reilly Media; Mourtou, E., Abdel-Badeeh, M.S., Pavlidis, G., Modelling and analyzing a hospital procedure using a Petri-net approach Proc. Int. Conf. World Acad. Sci., Eng. Technol. CESSE, 25, pp. 98-102. , Venice, Italy Nov. 2007; Nie, H.C., Lu, X.D., Duan, H.L., Zhang, J.Y., Integration of IHEbased systems with Petri net workflow management system (2009) Proc. 2nd Int. Conf. Biomed. Eng. Informat., pp. 1-5; Peterson, J.L., (1981) Petri Net Theory and the Modeling of Systems., , Englewood Cliffs NJ: Prentice-Hall; Recalde, L., Julvez, J., Silva, M., Steady state performance evaluation for some continuous Petri nets (2002) Proc. 15th IFAC World Congr., , Barcelona, Spain; Rohleder, T.R., Lewkonia, P., Bischak, D.P., Duffy, P., Hendijani, R., Using simulation modeling to improve patient flow at an outpatient orthopedic clinic Health Care Manage. Sci., 14 (2), pp. 135-145. , Jun. 2011; Roux, O., Combes, C., Duvivier, D., A modeling methodology dedicated to simulation and based on generic meta-models using MDA-UML: An application to a chronic renal dialysis unit Proc. Int. Conf. Service Syst. Service Manage., pp. 692-697. , Oct. 2006; Siau, K., Health care informatics IEEE Trans. Inf. Technol. Biomed., 7 (1), pp. 1-7. , Mar. 2003; Silva, M., Recalde, L., On the fluidification of petri nets: From discrete to hybrid and continuous models (2004) Annu. Rev. Control, 28 (2), pp. 253-266; Silva, M., Recalde, L., Unforced continuous Petri nets and positive systems (2003) Proc. 1st Multidisciplinary Int. Symp. POSTA, p. 294. , A. De Santis L. Benvenuti, and L. Farina, Eds. Springer- Verlag, Heidelberg, Germany; Silva, M., Recalde, L., Petri nets and integrality relaxations: A view of continuous Petri nets IEEE Trans. Syst., Man, Cybern. C, Appl. Rev., 32 (4), pp. 314-327. , Nov. 2002; Spyropoulos, C.D., AI planning and scheduling in the medical hospital environment Artif. Intell. Med., 20 (2), pp. 101-111. , Oct. 2000; Staccini, P., Joubert, M., Quaranta, J.-F., Fieschi, M., Mapping care processes within a hospital: From theory to a web-based proposal merging enterprise modelling and ISO normative principles Int. J. Med. Informat., 74 (2-4), pp. 335-344. , Mar. 2005; Swisher, J.R., Jacobson, S.H., Jun, J.B., Balci, O., Modeling and analyzing a physician clinic environment using discrete-event (visual) simulation Comput. Oper. Res., 28 (2), pp. 105-125. , Feb. 2001; Tang, Z.-W., Zong, D., Modeling business process reengineering through extended Petri net (2008) Proc. 4th ICNC, 2, pp. 458-463; Tasseva, V., Peneva, D., Atanassov, K., El-Darzi, E., Chountas, P., Vasilakis, C., Generalized net model for outpatient care in Bulgaria (2007) Proceedings - IEEE Symposium on Computer-Based Medical Systems, pp. 701-706. , DOI 10.1109/CBMS.2007.52, 4262730, Proceedings - Twentieth IEEE International Symposium on Computer-Based Medical Systems, CBMS'07; (2006) The Mathworks, , Math Works Inc. MATLAB Release Notes for Release 14 Natick, MA; Van Merode, G.G., Groothuis, S., Hasman, A., Enterprise resource planning for hospitals Int. J. Med. Informat., 73 (6), pp. 493-501. , Jun. 2004; Wang, T., Guinet, A., Belaidi, A., Besombes, B., Modelling and simulation of emergency services with ARIS and Arena. Case study: The emergency department of Saint Joseph and Saint Luc Hospital Prod. Planning Control, 20 (6), pp. 484-495. , Sep. 2009; Xiong, H.H., Zhou, M.C., Manikopoulos, C.N., Modeling and performance analysis of medical services systems using Petri nets (1994) Proc. IEEE Int. Conf. Syst., Man Cybern., pp. 2339-2342; Ye, Y., Jiang, Z.B., Diao, X.D., Du, G., Knowledge-based hybrid variance handling for patient care workflows based on clinical pathways (2009) Proc. Int. Conf.IEEE/INFORMS SOLI, pp. 13-18; Young, T., An agenda for healthcare and information simulation Health Care Manage. Sci., 8 (3), pp. 189-196. , Aug. 2005; Zhang, Y., Solving large-scale linear programs by interior-point methods under the MATLAB environment Dept. Math. Stat., , Univ. Maryland, Baltimore County, MD, Tech. Rep. TR96-01 Jul. 1995}, document_type={Article}, source={Scopus}, }`

- Costantino, N., Dotoli, M., Falagario, M., Fanti, M. P., Mangini, A. M., Sciancalepore, F. & Ukovich, W. (2013) A hierarchical optimization technique for the strategic design of distribution networks. IN Computers and Industrial Engineering, 66.849-864.

[Bibtex]`@ARTICLE{Costantino2013849, author={Costantino, N. and Dotoli, M. and Falagario, M. and Fanti, M.P. and Mangini, A.M. and Sciancalepore, F. and Ukovich, W.}, title={A hierarchical optimization technique for the strategic design of distribution networks}, journal={Computers and Industrial Engineering}, year={2013}, volume={66}, number={4}, pages={849-864}, doi={10.1016/j.cie.2013.09.009}, note={cited By 15}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84888288538&doi=10.1016%2fj.cie.2013.09.009&partnerID=40&md5=187777eb599e31cf44e2b5af060c6ecd}, abstract={The paper addresses the optimal design of the last supply chain branch, i.e., the Distribution Network (DN), starting from manufacturers till the retailers. It considers a distributed system composed of different stages connected by material links labeled with suitable performance indices. A hierarchical procedure employing direct graph (digraph) modeling, mixed integer linear programming, and the Analytic Hierarchy Process (AHP) is presented to select the optimal DN configuration. More in detail, a first-level DN optimization problem taking into account the definition and evaluation of the distribution chain performance provides a set of Pareto optimal solutions defined by digraph modeling. A second level DN optimization using the AHP method selects, on the basis of further criteria, the DN configuration from the Pareto face alternatives. To show the method effectiveness, the optimization model is applied to a case study describing an Italian regional healthcare drug DN. The problem solution by the proposed design method allows improving the DN flexibility and performance. © 2013 Elsevier Ltd. All rights reserved.}, author_keywords={Analytic hierarchy process; Digraph modeling; Distribution network; Mixed integer linear programming; Optimization; Supply chain}, keywords={Analytic hierarchy process (ahp); Digraph models; Hierarchical optimization; Mixed integer linear programming; Optimization modeling; Optimization problems; Pareto optimal solutions; Performance indices, Analytic hierarchy process; Directed graphs; Electric power distribution; Hierarchical systems; Linear programming; Optimal systems; Supply chains, Optimization}, references={Afshari, H., Amin-Najeri, M., Jaafari, A.A., A multi-objective approach for multi-commodity location within distribution network design problem (2010) Proc. Int. Multiconf. Engin. Comp. Scientists, , Hong Kong; Ambrosino, D., Grazia Scutella, M., Distribution network design: New problems and related models (2005) European Journal of Operational Research, 165 (3), pp. 610-624. , DOI 10.1016/j.ejor.2003.04.009, PII S0377221704000967; Chopra, S., Designing the distribution network in a supply chain (2003) Transportation Research Part E: Logistics and Transportation Review, 39 (2), pp. 123-140. , DOI 10.1016/S1366-5545(02)00044-3, PII S1366554502000443; Costantino, N., Dotoli, M., Falagario, M., Fanti, M.P., Mangini, A.M., A model for supply management of agile manufacturing supply chains (2012) International Journal of Production Economics, 135 (1), pp. 451-457; Costantino, N., Dotoli, M., Falagario, M., Fanti, M.P., Mangini, A.M., Sciancalepore, F., Ukovich, W., A model for the optimal design of the hospital drug distribution system (2010) Proc. IEEE Workshop on Health Care Management, , Venice (Italy); Costantino, N., Dotoli, M., Falagario, M., Fanti, M.P., Mangini, A.M., Sciancalepore, F., Ukovich, W., A model for the strategic design of distribution networks (2010) Proc. IEEE Conf. Aut. Sci. Eng., p. 6. , Toronto (Canada); Costantino, N., Dotoli, M., Falagario, M., Fanti, M.P., Mangini, A.M., Sciancalepore, F., Ukovich, W., A fuzzy programming approach for the strategic design of distribution networks (2011) Proc. IEEE Conf. Aut. Sci. Eng., p. 6. , Trieste (Italy); Davidrajuh, R., Ma, H., Developing a modern distribution chain: A three-pronged approach (2006) 2006 IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI 2006, pp. 340-345. , DOI 10.1109/SOLI.2006.236140, 1700889, 2006 IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI 2006; Dotoli, M., Fanti, M.P., Iacobellis, G., Mangini, A.M., A first order hybrid Petri net model for supply chain management (2009) IEEE Transactions on Automation Science and Engineering, 6 (4), pp. 744-758; Dotoli, M., Fanti, M.P., Meloni, C., Zhou, M.C., A multi-level approach for network design of integrated supply chains (2005) International Journal of Production Research, 43 (20), pp. 4267-4287. , DOI 10.1080/00207540500142316; Dotoli, M., Fanti, M.P., Meloni, C., Zhou, M.C., Design and optimization of integrated e-supply chain for agile and environmentally conscious manufacturing (2006) IEEE Transactions on Systems, Man, and Cybernetics Part A:Systems and Humans, 36 (1), pp. 62-75. , DOI 10.1109/TSMCA.2005.859189; Ehrgott, M., (2000) Multicriteria Optimization, , Springer-Verlag Berlin-Heidelberg; Gaonkar, R.S., Viswanadham, N., Strategic sourcing and collaborative planning in internet-enabled supply chain networks producing multigeneration products (2005) IEEE Transactions on Automation Science and Engineering, 2 (1); http://www.gnu.org/software/glpk/glpk.html, Gnu Linear Programming Kit. 2012; Jang, Y.-J., Jang, S.-Y., Chang, B.-M., Park, J., A combined model of network design and production/distribution planning for a supply network (2002) Computers and Industrial Engineering, 43 (1-2), pp. 263-281. , DOI 10.1016/S0360-8352(02)00074-8, PII S0360835202000748; Luo, Y., Zhou, M., Caudill, R.J., An integrated E-supply chain model for agile and environmentally conscious manufacturing (2001) IEEE/ASME Transactions on Mechatronics, 6 (4), pp. 377-386. , DOI 10.1109/3516.974851, PII S1083443501107453; Min, H., Zhou, G., Supply chain modeling: Past, present and future (2002) Computers and Industrial Engineering, 43 (1-2), pp. 231-249. , DOI 10.1016/S0360-8352(02)00066-9, PII S0360835202000669; Miranda, P.A., Garrido, R.A., Ceroni, J.A., E-work based collaborative optimization approach for strategic logistic network design problem (2009) Computers & Industrial Engineering, 57, pp. 3-13; Nagurney, A., Optimal supply chain network design and redesign at minimal total cost and with demand satisfaction (2010) International Journal of Production Economics, 128, pp. 200-208; Nagurney, A., Supply chain network design under profit maximization and oligopolistic competition (2010) Transportation Research Part e, 46, pp. 281-294; Nunkaew, W., Phruksaphanrat, B., A multiobjective programming for transportation problem with the consideration of both depot to customer and customer to customer relationships (2009) Proc. Int. Multiconf. Engin. Comp. Scientists, , Hong Kong; Saaty, T.L., (2004) Mathematical Methods of Operational Research, , Courier Dover Publications; Saaty, T.L., Decision making with the analytic hierarchy process (2008) International Journal of Services Sciences, 1 (1), pp. 83-98; Stein, W.E., Mizzi, P.J., The harmonic consistency index for the analytic hierarchy process (2007) European Journal of Operational Research, 177 (1), pp. 488-497. , DOI 10.1016/j.ejor.2005.10.057, PII S0377221705009288; Tanonkou, G.-A., Benyoucef, L., Xie, X., Design of stochastic distribution networks using Lagrangian relaxation (2008) IEEE Transactions on Automation Science and Engineering, 5 (4); Zanjirani, F.R., Elahipanaha, M., A genetic algorithm to optimize the total cost and service level for just-in-time distribution in a supply chain (2008) International Journal of Production Economics, 111, pp. 229-243}, document_type={Article}, source={Scopus}, }`

- Dotoli, M., Sciancalepore, F., Epicoco, N., Falagario, M., Turchiano, B. & Costantino, N. (2013) A periodic event scheduling approach for offline timetable optimization of regional railways IN 2013 10th IEEE International Conference on Networking, Sensing and Control, ICNSC 2013., 849-854.

[Bibtex]`@CONFERENCE{Dotoli2013849, author={Dotoli, M. and Sciancalepore, F. and Epicoco, N. and Falagario, M. and Turchiano, B. and Costantino, N.}, title={A periodic event scheduling approach for offline timetable optimization of regional railways}, journal={2013 10th IEEE International Conference on Networking, Sensing and Control, ICNSC 2013}, year={2013}, pages={849-854}, doi={10.1109/ICNSC.2013.6548849}, art_number={6548849}, note={cited By 9}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84881269306&doi=10.1109%2fICNSC.2013.6548849&partnerID=40&md5=e184d97b5e9540259e36989575a8073c}, abstract={We address the train timetabling problem for regional rails by a cyclic scheduling approach. We revisit a mixed integer linear programming model for offline timetable optimization and enhance it using a discrete event formulation and taking into account single-track stations that typically characterize local rails. The model can be applied to regional railways that are increasingly gaining significance due to the social pressure for sustainable mobility. The approach is successfully applied to a large portion of a real Southern Italy railway network, obtaining a timetable that enhances the passengers service level. © 2013 IEEE.}, author_keywords={events; management; optimization; Railways; regional networks; scheduling algorithms; train timetable}, keywords={Cyclic scheduling; events; Mixed integer linear programming model; Railways; Regional networks; Sustainable mobility; Train timetables; Train timetabling problem, Discrete event simulation; Linear programming; Management; Optimization; Railroads; Scheduling; Scheduling algorithms, Railroad transportation}, references={Bevilacqua, V., Costantino, N., Dotoli, M., Falagario, M., Sciancalepore, F., Strategic design and multi-objective optimization of distribution networks based on genetic algorithms (2012) Int. J. Comp. Int. Manuf, 25, pp. 1139-1150; Boschian, V., Dotoli, M., Fanti, M.P., Iacobellis, G., Ukovich, W., A metamodelling approach to the management of intermodal transportation networks (2011) IEEE Trans. Aut. Sci. Eng, 8, pp. 457-469; Cacchiani, V., Toth, P., Nominal and robust train timetabling problems (2012) Eur. J. Op. Res, 219, pp. 727-737; Caprara, A., Monaci, M., Toth, P., Guida, P.L., A Lagrangian heuristic algorithm for a real-world train timetabling problem (2006) Disc. Appl. Math, 154, pp. 738-753; Costantino, N., Dotoli, M., Falagario, M., Fanti, M.P., Mangini, A.M., A model for supply management of agile manufacturing supply chains (2012) Int. J. Prod. Econ, 135, pp. 451-457; Costantino, N., Dotoli, M., Falagario, M., Fanti, M.P., Mangini, A.M., Sciancalepore, F., Ukovich, W., A Fuzzy Programming Approach for the Strategic Design of Distribution Networks (2011) Proc. IEEE CASE 2011, p. 6. , Trieste, Italy, August 24-27; Dotoli, M., Fanti, M.P., Mangini, A.M., Stecco, G., Ukovich, W., The impact of ICT on intermodal transportation systems: A modelling approach by Petri nets (2010) Contr. Eng. Prac, 18, pp. 893-903; Linear Programming G, http://www.gnu.org/software/glpk/; Huisman, D., Kroon, L.G., Lentink, R.M., Vromans, M.J.C.M., Operations Research in Passenger Railway Transportation (2005) Statistica Neerlandica, 59, pp. 467-497; Lee, Y., Chen, C.-Y., A heuristic for the train pathing and timetabling problem (2009) Transp. Res. B, 43, pp. 837-851; Liebchen, C., Schachtebeck, M., Schöbel, A., Stiller, S., Prigge, A., Computing delay resistant railway timetables (2010) Comp. Oper. Res, 37, pp. 857-868; Odijk, M., A constraint generation algorithm for the construction of periodic railway timetables (1996) Transp. Res. B, 30, pp. 455-464; Peeters, L.W.P., (2003) Cycling Railway Timetable Optimization, , PhD thesis, Erasmus University, Rotterdam; Serafini, P., Ukovich, W., A mathematical model for periodic event scheduling problems (1989) SIAM J. Disc. Math, 2, pp. 550-581}, document_type={Conference Paper}, source={Scopus}, }`

- Dotoli, M., Hammadi, S., Jeribi, K., Russo, C. & Zgaya, H. (2013) A multi-agent Decision Support System for optimization of co-modal transportation route planning services IN Proceedings of the IEEE Conference on Decision and Control., 911-916.

[Bibtex]`@CONFERENCE{Dotoli2013911, author={Dotoli, M. and Hammadi, S. and Jeribi, K. and Russo, C. and Zgaya, H.}, title={A multi-agent Decision Support System for optimization of co-modal transportation route planning services}, journal={Proceedings of the IEEE Conference on Decision and Control}, year={2013}, pages={911-916}, doi={10.1109/CDC.2013.6759998}, art_number={6759998}, note={cited By 10}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84902324424&doi=10.1109%2fCDC.2013.6759998&partnerID=40&md5=e506e71d6e9b41c8f5512862f40db5ec}, abstract={We present a Decision Support System (DSS) for co-modal transportation. The DSS answers multiple route planning requests in a co-modal setting with vehicle preference and conflicting criteria, e.g., costs, time, and gas emissions minimization. The DSS architecture is based on the inherently distributed multi-agent systems framework that allows the decomposition of the route planning problem into multiple simpler tasks. A genetic algorithm is employed to obtain the optimal user-vehicle-route combinations according to the users preferences. The DSS is tested simulating itinerary requests with conflicting preferences in Nord Pas de Calais (France). © 2013 IEEE.}, keywords={Genetic algorithms; Multi agent systems, Conflicting preferences; Decision support system (dss); Distributed multiagent systems; Multiple routes; Route planning, Decision support systems}, references={Ayed, H., Galvez-Fernandez, C., Habbas, Z., Khadraoui, D., Solving time-dependent multimodal transport problems using a transfer graph model (2011) Comp. Ind. Eng., 61, pp. 391-401; Chen, B., Cheng, H.H., A review of the applications of agent technology in traffic and transportation systems (2010) IEEE Trans. Intell. Transp. Sys., 11, pp. 485-497; Chiu, D.K.W., Lee, O.K.F., Leung, H.-F., Au, E.W.K., Wong, M.C.W., A multi-modal agent based mobile route advisory system for public transport network (2005) Proc. Hawaii Int. Conf. Sys. Sci., 10; Ehrgott, M., (2000) Multicriteria Optimization, , Springer- Verlag; Foo, H.M., Leong, H.W., Lao, Y., Lau, H.C., A multi-criteria, multimodal passenger route advisory system (1999) Proc. IES-CTR, 17; (2001) European Transport Policy for 2010: Time to Decide, , http://europa.eu.int, White Paper, available; Giannopoulos, G.A., The application of co-modality in Greece: A critical appraisal of progress in development of co-modal freight centers and logistics services (2008) Trans. Stu. Rev., 15, pp. 289-301; Jeribi, K., Mejiri, H., Zgaya, H., Hammadi, S., Vehicle sharing services optimization based on multi-agent approach (2011) 18th IFAC Wor. Congr., pp. 13040-13045; Jeribi, K., Mejri, H., Zgaya, H., Hammadi, S., Distributed graphs for solving co-modal transport problems (2011) 14th Int. IEEE Conf. Intell. Transp. Sys., pp. 1150-1155; Michalewicz, Z., Fogel, D.B., (2004) How to Solve It: Modern Heuristics, , Springer-Verlag; Power, D.J., Sharda, R., (2009) Springer Handbook of Automation, , PRISM Center School of Industrial Engineering, Purdue Univ; http://www.ratp.fr; Russell, S.J., Norvig, P., (2009) Artificial Intelligence: A Modern Approach, , Pearson, rd Ed; Tumas, G., Ricci, F., Personalized mobile city transport advisory system (2009) Information and Communication Technologies in Tourism, pp. 173-184. , Springer; Wang, J., Kaempke, T., Shortest route computation in distributed systems (2004) Comp. Oper. Res., 31, pp. 1621-1633; Wang, F., Agent-based control for networked traffic management systems (2005) IEEE Intell. Sys., 20, pp. 92-96}, document_type={Conference Paper}, source={Scopus}, }`

### 2012

- Costantino, N., Dotoli, M., Epicoco, N., Falagario, M. & Sciancalepore, F. (2012) A cross efficiency fuzzy data envelopment analysis technique for supplier evaluation under uncertainty IN IEEE International Conference on Emerging Technologies and Factory Automation, ETFA..

[Bibtex]`@CONFERENCE{Costantino2012, author={Costantino, N. and Dotoli, M. and Epicoco, N. and Falagario, M. and Sciancalepore, F.}, title={A cross efficiency fuzzy data envelopment analysis technique for supplier evaluation under uncertainty}, journal={IEEE International Conference on Emerging Technologies and Factory Automation, ETFA}, year={2012}, doi={10.1109/ETFA.2012.6489600}, art_number={6489600}, note={cited By 7}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84876391183&doi=10.1109%2fETFA.2012.6489600&partnerID=40&md5=b3dd1395d6852890b4f3d746d87b1632}, abstract={We present a novel cross efficiency fuzzy Data Envelopment Analysis (DEA) technique for supplier selection under uncertainty. In order to deal with uncertain input and output suppliers data, triangular fuzzy numbers are employed. A fuzzy triangular efficiency is associated to each supplier through a cross evaluation by a compromise between objectives. The results are defuzzified and a supplier ranking is determined. The method is applied to the evaluation of a set of candidate suppliers of an Italian SME, showing the ease of application and discriminative power among suppliers. © 2012 IEEE.}, keywords={Cross efficiency; Cross evaluation; Discriminative power; Fuzzy data envelopment analysis; Input and outputs; Supplier Evaluations; Supplier selection; Triangular fuzzy numbers, Data envelopment analysis; Factory automation; Fuzzy sets, Efficiency}, references={Charnes, A., Cooper, W.W., Rhodes, E., Measuring the efficiency of decision making units (1978) European Journal of Operational Research, 2, pp. 429-444; Costantino, N., Dotoli, M., Falagario, M., Fanti, M.P., Mangini, A.M., Sciancalepore, F., Ukovich, W., A fuzzy programming approach for the strategic design of distribution networks (2011) Proc. IEEE Conf. on Automation Science and Engineering (CASE), Trieste (Italy), , 24-27 August; Costantino, N., Dotoli, M., Falagario, M., Fanti, M.P., Mangini, A.M., Sciancalepore, F., Supplier selection in the public procurement sector via a data envelopment analysis approach (2011) Proc. 19th Annual IEEE Mediterranean Conf.E on Control and Automation (MED 2011), Corfu, Greece, , June 23-25; Costantino, N., Dotoli, M., Falagario, M., Sciancalepore, F., A model using data envelopment analysis for the cross evaluation of suppliers under uncertainty (2011) Proc. Int. Conf. on Knowledge Management and Information Sharing (KMIS 2011), Paris, France, , October 26-29; De Boer, L., Labro, E., Morlacchi, P., A review of methods supporting supplier selection (2001) European Journal of Purchasing and Supply Management, 7, pp. 75-89; Guo, P., Tanaka, H., Fuzzy dea: A perceptual evaluation method (2001) Fuzzy Sets and Systems, 119 (1), pp. 149-160; Hatami-Marbini, A., Saati, S., Makui, A., Ideal and anti-ideal decision making units: A fuzzy dea approach (2010) Journal of Industrial Engineering International, 6 (10), pp. 31-41; Hatami-Marbini, A., Emrouznejad, A., Tavana, M., A taxonomy and review of the fuzzy data envelopment analysis literature: Two decades in the making (2011) European Journal of Operational Research, 214, pp. 457-472; Ho, W., Xu, X., Dey, P.K., Multi-criteria decision making approaches for supplier evaluation and selection: A literature review European Journal of Operational Research, 202, pp. 16-24; Jimenez, M., Bilbao, A., Pareto-optimal solution in fuzzy multiobjective linear programming (2009) Fuzzy Sets and Systems, 160, pp. 2714-2721; Kabak, Ö., Ülengin, F., Possibilistic linear-programming approach for supply chain networking decisions (2011) European Journal of Operational Research, 209, pp. 253-264; Khodabakhshi, M., Gholami, Y., Kheirollahi, H., An additive model approach for estimating returns to scale in imprecise data envelopment analysis Applied Mathematical Modelling, 34, pp. 1247-1257; Leon, T., Liern, V., Ruiz, J.L., Sirvent, I., A fuzzy mathematical programming approach to the assessment of efficiency with dea models (2003) Fuzzy Sets and Systems, 139 (2), pp. 407-419; Lertworasirikul, S., Fang, S.C., Joines, J.A., Nuttle, H.L.W., Fuzzy data envelopment analysis (dea): A possibility approach (2003) Fuzzy Sets and Systems, 139 (2), pp. 379-394; Lertworasirikul, S., Fang, S.C., Joines, J.A., Nuttle, H.L.W., Fuzzy data envelopment analysis (fuzzy dea): A credibility approach (2003) Fuzzy Sets Based Heuristics for Optimization, Physica Verlag, pp. 141-158. , Verdegay, J.L. (Ed.); Li, L., Zabinsky, Z.B., Incorporating uncertainty into a supplier selection problem (2011) International Journal of Production Economics, 134, pp. 344-356; Liang, T.-F., Application of fuzzy sets to manufacturing/distribution planning decisions in supply chains (2011) Information Science, 181, pp. 842-854; Luban, F., Measuring efficiency of a hierarchical organization with fuzzy dea method (2009) Economia, Seria Management, 12 (1), pp. 87-97; Owens Swift, C., Preferences for single sourcing and supplier selection criteria (1995) J. Bus. Res., 32, pp. 105-111; Pfohl, H.-C., Köhler, H., Thomas, D., State of the art in supply chain risk management research: Empirical and conceptual findings and a roadmap for the implementation in practice (2010) Logistics Research, 2, pp. 33-44; Qin, R., Liu, Y.K., A new data envelopment analysis model with fuzzy random inputs and outputs (2009) Journal of Applied Mathematics and Computing, 33 (1-2), pp. 327-356; Qin, R., Liu, Y.K., Modeling data envelopment analysis by chance method in hybrid uncertain environments (2010) Mathematics and Computers in Simulation, 80 (5), pp. 922-950; Sengupta, J.K., A fuzzy systems approach in data envelopment analysis (1992) Computers and Mathematics with Applications, 24 (8-9), pp. 259-266; Sheth, N., Triantis, K., Measuring and evaluating efficiency and effectiveness using goal programming and data envelopment analysis in a fuzzy environment (2003) Yugoslav Journal of Operations Research, 13 (1), pp. 35-60; Soleimani-Damaneh, M., Jahanshahloo, G.R., Abbasbandy, S., Computational and theoretical pitfalls in some current performance measurement techniques and a new approach Applied Mathematics and Computation, 181 (2), pp. 1199-1207; Tsaur, S.-H., Chang, T.-Y., Yen, C.-H., The evaluation of airline service quality by fuzzy mcdm (2002) Tourism Management, 23, pp. 107-115; Ufuk Bilsel, R., Ravindran, A., A multiobjective chance constrained programming model for supplier selection under uncertainty (2011) Transportation Research Part B: Methodological, 45 (8), pp. 1284-1300; Wang, Y.M., Luo, Y., Liang, L., Fuzzy data envelopment analysis based upon fuzzy arithmetic with an application to performance assessment of manufacturing enterprises (2009) Expert Systems with Applications, 36, pp. 5205-5211; Wen, M., You, C., Kang, R., A new ranking method to fuzzy data envelopment analysis (2011) Computers & Mathematics with Applications, 59 (11), pp. 3398-3404; Zhang, X., Zhang, L., Supplier selection and purchase problem with fixed cost and constrained order quantities under stochastic demand (2011) International Journal of Production Economics, 129 (1), pp. 1-7; Zimmermann, H.-J., (2001) Fuzzy Set Theory and Its Applications, , 4th Ed, Kluwer Academic Publishers, Boston/Dordrecht/London; Araz, C., Ozkarahan, I., Supplier evaluation and management system for strategic sourcing based on a new multicriteria sorting procedure (2007) International Journal of Production Economics, 106, pp. 585-606; Sexton, T.R., Silkman, R.H., Hogan, A.J., Data envelopment analysis: Critique and extensions (1986) Measuring Efficiency: An Assessment of Data Envelopment Analysis, , Silkman, R.H. (Ed.), Jossey- Bass, San Francisco, CA; Wang, J.M., Chin, K.S., A neutral dea model for cross-efficiency evaluation and its extension (2010) Expert Systems with Applications, 37, pp. 3666-3675; Doyle, J., Green, R., Efficiency and cross-efficiency in dea: Derivation, meanings and uses (1994) Journal of Operational Research Society, 45, pp. 567-578; Costantino, N., Dotoli, M., Epicoco, N., Falagario, M., Sciancalepore, F., A novel fuzzy data envelopment analysis methodology for performance evaluation in a two-stage supply chain (2012) Proc. 8th Annual IEEE Conference on Automation Science and Engineering (CASE 2012), Seoul, Korea, , August 20-24; Gnu Linear Programming Kit, , http://www.gnu.org/software/glpk/glpk.html, available at}, document_type={Conference Paper}, source={Scopus}, }`

- Cabasino, M. P., Dotoli, M. & Seatzu, C. (2012) Marking estimation of fuzzy Petri nets IN IEEE International Conference on Emerging Technologies and Factory Automation, ETFA..

[Bibtex]`@CONFERENCE{Cabasino2012, author={Cabasino, M.P. and Dotoli, M. and Seatzu, C.}, title={Marking estimation of fuzzy Petri nets}, journal={IEEE International Conference on Emerging Technologies and Factory Automation, ETFA}, year={2012}, doi={10.1109/ETFA.2012.6489737}, art_number={6489737}, note={cited By 0}, url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84876358726&doi=10.1109%2fETFA.2012.6489737&partnerID=40&md5=8dfebfd27061e28bf211aaa621c20559}, abstract={In this paper we deal with the problem of designing an observer for Petri nets under the assumption that all transitions may be observed but there exist some uncertainties in the initial marking. In particular, the information on the initial marking is given in terms of fuzzy markings by associating a discrete membership function with each place. Some ideas on how to extend the proposed approach to the case of unobservable transitions are also discussed. © 2012 IEEE.}, keywords={Fuzzy Petri nets; Initial marking; Marking estimation; Unobservable, Factory automation, Petri nets}, references={Ammar, H., Yu, L., Fuzzy marking petri nets: Concepts and definitions (1995) Proc. IEEE Int. Symposium on Intelligent Control, pp. 291-297. , Aug; Cabasino, M., Giua, A., Pocci, M., Seatzu, C., Discrete event diagnosis using labeled petri nets (2011) An Application to Manufacturing Systems. Control Engineering Practice, 19 (9), pp. 989-1001; Giua, A., Seatzu, C., Observability of place/transition nets (2012) IEEE Trans. on Aut. Control, 47 (9), pp. 1424-1437; González-Castolo, J., López-Mellado, E., State estimation of partially observable des using fuzzy timed petri nets (2011) Proc. IEEE 16th Conf. on Emerging Tech. & Factory Automation, pp. 1-8. , Toulouse, France, Sept; Z.L.A. fuzzy sets as a basis for a theory of possibility (1978) Fuzzy Sets and Systems, 1, pp. 3-28. , Reprinted in Fuzzy Sets and Systems 100 Supplement 9-34`