Mariagrazia DOTOLI


Biografia

Mariagrazia Dotoli is a Full Professor in Systems and Control Engineering at Politecnico di Bari, Department of Electrical and Information Engineering, which she joined in 1999 as a tenured Assistant Professor. She was the 2011-2013 Vice Rector for Research of Politecnico di Bari (Italy) and a 2012-2015 member elect of the Academic Senate of the same University. She is currently Vice Director for Research of the Department of Electrical and Information Engineering at Politecnico di Bari. She received the Laurea degree in Electronic Engineering with honors in 1995 and the Ph.D. in Electrical Engineering in 1999, both from Politecnico di Bari.

She has been a visiting scholar at the Paris 6 University (France) and at the Technical University of Denmark. Since 2003 she is an expert evaluator of the European Commission, previously for the Sixth and Seventh RTD Framework Programmes, and subsequently for Horizon 2020. Her research interests include the modeling, identification, management, control, automation, optimization, and diagnosis of: discrete event industrial systems, Petri nets, manufacturing systems, supply chains, logistics and transportation systems, traffic networks, energy systems.

She is an Associate Editor of the IEEE Transactions on Automation Science and Engineering, the IEEE Transactions on Control Systems Technology, and the IEEE Transactions on Systems, Man, and Cybernetics: Systems. She was an Associate Editor of the IEEE Robotics and Automation Letters until 2018.

She is currently General Chair of the MED2021 29th Mediterranean Conference on Control and Automation, Program Chair of the CODIT2020 International Conference on Control, Decision and Information Technologies, Program Chair of the CASE2020 annual IEEE Conference on Automation Science and Engineering and Publicity Co-Chair of the IEEE International Conference on Systems, Man, and Cybernetics.

She was the Workshop and Tutorial chair of the 2015 IEEE Conference on Automation Science and Engineering, the Special Session co-chair of the 2013 IEEE Conference on Emerging Technology and Factory Automation, and chair of the National Committee of the 2009 IFAC Workshop on Dependable Control of Discrete Systems. She has been member of the International Program Committee of 70+ international Conferences and Symposia. She is a member of the following committees: IFAC Technical Committee on Discrete Event and Hybrid Systems (since 2011); IEEE Systems Man and Cybernetics Society Technical Committee on Discrete Event Systems (since 2007); IEEE Control Systems Society Technical Committee on Discrete Event Systems (since 2005).

She is author or co-author of 200+ printed publications, including 1 book (in Italian) and 70+ papers on international peer reviewed journals. Her Scopus Researcher page is available at http://www.scopus.com/authid/detail.url?authorId=6603204493.

Temi di ricerca

  • automation;
  • optimization;
  • discrete event industrial systems;
  • decision and control systems;
  • modeling and optimization of complex systems;
  • petri nets;
  • manufacturing systems;
  • supply chains;
  • logistics and transportation systems;
  • traffic networks;
  • energy systems.

Pubblicazioni

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. doi:10.1109/MED51440.2021.9480359
    [BibTeX] [Abstract] [Download PDF]
    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.
    @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. doi:10.1109/MED51440.2021.9480179
    [BibTeX] [Abstract] [Download PDF]
    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.
    @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. doi:10.1109/MED51440.2021.9480332
    [BibTeX] [Abstract] [Download PDF]
    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.
    @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. doi:10.1109/MED51440.2021.9480353
    [BibTeX] [Abstract] [Download PDF]
    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.
    @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. doi:10.1109/MED51440.2021.9480319
    [BibTeX] [Abstract] [Download PDF]
    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.
    @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. doi:10.1109/TASE.2020.2986269
    [BibTeX] [Abstract] [Download PDF]
    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.
    @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},
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    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, .. doi:10.1109/TASE.2021.3072862
    [BibTeX] [Abstract] [Download PDF]
    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
    @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, .. doi:10.1109/TCST.2021.3056751
    [BibTeX] [Abstract] [Download PDF]
    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
    @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. doi:10.1109/TSMC.2018.2854620
    [BibTeX] [Abstract] [Download PDF]
    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.
    @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},
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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. 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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},
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    }
  • 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. doi:10.1109/SMC42975.2020.9283440
    [BibTeX] [Abstract] [Download PDF]
    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.
    @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.. doi:10.23919/AEIT50178.2020.9241136
    [BibTeX] [Abstract] [Download PDF]
    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.
    @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. doi:10.1109/MED48518.2020.9183012
    [BibTeX] [Abstract] [Download PDF]
    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.
    @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. doi:10.1109/MED48518.2020.9182937
    [BibTeX] [Abstract] [Download PDF]
    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.
    @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.. doi:10.3390/en13174586
    [BibTeX] [Abstract] [Download PDF]
    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/).
    @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},
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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. 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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. 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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. 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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. doi:10.1109/CASE48305.2020.9216800
    [BibTeX] [Abstract] [Download PDF]
    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.
    @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. doi:10.1109/CASE48305.2020.9216875
    [BibTeX] [Abstract] [Download PDF]
    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.
    @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. doi:10.1109/TASE.2020.2990785
    [BibTeX] [Download PDF]
    @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. doi:10.1109/TASE.2020.2977452
    [BibTeX] [Abstract] [Download PDF]
    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.
    @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. 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    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. doi:10.1109/TASE.2020.2966738
    [BibTeX] [Abstract] [Download PDF]
    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.
    @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},
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[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. doi:10.1109/CoDIT49905.2020.9263874
    [BibTeX] [Abstract] [Download PDF]
    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.
    @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.. doi:10.1109/CoDIT49905.2020.9263895
    [BibTeX] [Download PDF]
    @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. doi:10.1109/CoDIT49905.2020.9263914
    [BibTeX] [Abstract] [Download PDF]
    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.
    @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.. doi:10.1016/j.energy.2020.117188
    [BibTeX] [Abstract] [Download PDF]
    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
    @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},
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    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.. doi:10.3390/SU12083111
    [BibTeX] [Abstract] [Download PDF]
    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.
    @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},
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    document_type={Article},
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  • 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.. doi:10.1016/j.asoc.2020.106064
    [BibTeX] [Abstract] [Download PDF]
    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.
    @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},
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  • 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.. doi:10.3390/s20030781
    [BibTeX] [Abstract] [Download PDF]
    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.
    @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. 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    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.. doi:10.1016/j.cie.2019.106217
    [BibTeX] [Abstract] [Download PDF]
    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
    @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},
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    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. doi:10.1016/j.ifacol.2020.12.1830
    [BibTeX] [Abstract] [Download PDF]
    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
    @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},
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    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, .. doi:10.1109/TASE.2020.3040940
    [BibTeX] [Abstract] [Download PDF]
    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
    @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. doi:10.1016/j.arcontrol.2020.09.005
    [BibTeX] [Abstract] [Download PDF]
    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
    @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},
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    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. doi:10.1007/978-3-030-61746-2_7
    [BibTeX] [Abstract] [Download PDF]
    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.
    @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},
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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. 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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. doi:10.1016/j.promfg.2020.02.041
    [BibTeX] [Abstract] [Download PDF]
    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.
    @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},
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  • 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. doi:10.1109/LCSYS.2019.2923078
    [BibTeX] [Abstract] [Download PDF]
    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.
    @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. doi:10.1109/SMC.2019.8914082
    [BibTeX] [Abstract] [Download PDF]
    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.
    @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. doi:10.1109/SMC.2019.8914489
    [BibTeX] [Abstract] [Download PDF]
    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.
    @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. doi:10.1109/SMC.2019.8913892
    [BibTeX] [Abstract] [Download PDF]
    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.
    @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. doi:10.1109/COASE.2019.8843230
    [BibTeX] [Abstract] [Download PDF]
    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.
    @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. doi:10.1109/COASE.2019.8843141
    [BibTeX] [Abstract] [Download PDF]
    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.
    @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. doi:10.23919/ECC.2019.8796182
    [BibTeX] [Abstract] [Download PDF]
    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.
    @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. doi:10.1109/JAS.2019.1911462
    [BibTeX] [Abstract] [Download PDF]
    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.
    @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},
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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. 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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. 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    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. doi:10.1109/CoDIT.2019.8820603
    [BibTeX] [Abstract] [Download PDF]
    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.
    @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. doi:10.1109/CoDIT.2019.8820380
    [BibTeX] [Abstract] [Download PDF]
    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.
    @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. doi:10.1109/CDC.2018.8619425
    [BibTeX] [Abstract] [Download PDF]
    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.
    @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. doi:10.1109/SMC.2018.00242
    [BibTeX] [Abstract] [Download PDF]
    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.
    @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] [Abstract] [Download PDF]
    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.
    @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.. doi:10.3390/app9051025
    [BibTeX] [Abstract] [Download PDF]
    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.
    @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. 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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. 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    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. doi:10.1080/00207543.2018.1510558
    [BibTeX] [Abstract] [Download PDF]
    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.
    @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},
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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. 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    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. doi:10.1109/COASE.2018.8560501
    [BibTeX] [Abstract] [Download PDF]
    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.
    @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. doi:10.1016/j.jenvman.2018.07.075
    [BibTeX] [Abstract] [Download PDF]
    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
    @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},
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    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. doi:10.1109/CCTA.2018.8511345
    [BibTeX] [Abstract] [Download PDF]
    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.
    @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. doi:10.1109/CCTA.2018.8511617
    [BibTeX] [Abstract] [Download PDF]
    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.
    @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},
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    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. doi:10.1016/j.cor.2017.11.016
    [BibTeX] [Abstract] [Download PDF]
    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
    @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},
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    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. doi:10.1109/TITS.2017.2737788
    [BibTeX] [Abstract] [Download PDF]
    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.
    @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},
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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. 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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. 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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. 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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. 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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. doi:10.1109/COASE.2017.8256063
    [BibTeX] [Download PDF]
    @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. doi:10.1016/j.ifacol.2018.07.065
    [BibTeX] [Abstract] [Download PDF]
    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
    @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. doi:10.1016/j.ifacol.2018.06.311
    [BibTeX] [Abstract] [Download PDF]
    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
    @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. doi:10.1016/j.ifacol.2018.07.061
    [BibTeX] [Abstract] [Download PDF]
    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
    @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. doi:10.1016/j.apm.2017.07.030
    [BibTeX] [Abstract] [Download PDF]
    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.
    @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. doi:10.1016/j.conengprac.2017.08.007
    [BibTeX] [Abstract] [Download PDF]
    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
    @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},
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    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. doi:10.1109/SOLI.2017.8120971
    [BibTeX] [Abstract] [Download PDF]
    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.
    @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. doi:10.1109/LCSYS.2017.2716190
    [BibTeX] [Abstract] [Download PDF]
    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.
    @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. doi:10.1109/TITS.2016.2645278
    [BibTeX] [Abstract] [Download PDF]
    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.
    @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},
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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. doi:10.1109/ICNSC.2017.8000150
    [BibTeX] [Abstract] [Download PDF]
    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.
    @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. doi:10.1109/COASE.2017.8256266
    [BibTeX] [Abstract] [Download PDF]
    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.
    @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. doi:10.1109/COASE.2017.8256140
    [BibTeX] [Abstract] [Download PDF]
    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.
    @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. doi:10.1016/j.ifacol.2017.08.2292
    [BibTeX] [Abstract] [Download PDF]
    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
    @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. doi:10.1016/j.ifacol.2017.08.2051
    [BibTeX] [Abstract] [Download PDF]
    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
    @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. doi:10.1016/j.ifacol.2017.08.1544
    [BibTeX] [Abstract] [Download PDF]
    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
    @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. doi:10.1109/TSMC.2016.2521836
    [BibTeX] [Abstract] [Download PDF]
    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.
    @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},
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  • 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. doi:10.1080/00207543.2016.1178408
    [BibTeX] [Abstract] [Download PDF]
    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.
    @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},
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    document_type={Article},
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    }
  • 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. doi:10.1109/TASE.2016.2593101
    [BibTeX] [Abstract] [Download PDF]
    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.
    @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},
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  • 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. doi:10.1109/TASE.2017.2670758
    [BibTeX] [Download PDF]
    @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. doi:10.1080/00207543.2016.1173259
    [BibTeX] [Abstract] [Download PDF]
    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.
    @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},
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    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. doi:10.1109/TSMC.2015.2506540
    [BibTeX] [Abstract] [Download PDF]
    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.
    @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},
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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. 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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. doi:10.1109/SMC.2016.7844253
    [BibTeX] [Abstract] [Download PDF]
    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.
    @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. doi:10.1109/SMC.2016.7844300
    [BibTeX] [Abstract] [Download PDF]
    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.
    @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. doi:10.1016/j.conengprac.2016.11.014
    [BibTeX] [Abstract] [Download PDF]
    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
    @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},
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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. 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    }
  • 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. doi:10.1109/ECC.2016.7810656
    [BibTeX] [Abstract] [Download PDF]
    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.
    @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.. doi:10.1109/EESMS.2016.7504820
    [BibTeX] [Abstract] [Download PDF]
    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.
    @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. doi:10.1111/itor.12155
    [BibTeX] [Abstract] [Download PDF]
    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.
    @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},
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    }
  • 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. doi:10.1109/WODES.2016.7497824
    [BibTeX] [Abstract] [Download PDF]
    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.
    @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. doi:10.1109/TASE.2015.2404438
    [BibTeX] [Abstract] [Download PDF]
    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.
    @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},
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  • 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. doi:10.1109/LRA.2015.2502905
    [BibTeX] [Abstract] [Download PDF]
    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.
    @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. doi:10.1109/ECC.2015.7331071
    [BibTeX] [Abstract] [Download PDF]
    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.
    @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.. doi:10.1109/ETFA.2015.7301509
    [BibTeX] [Abstract] [Download PDF]
    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.
    @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.. doi:10.1109/ETFA.2015.7301580
    [BibTeX] [Abstract] [Download PDF]
    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.
    @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.. doi:10.1109/ETFA.2015.7301435
    [BibTeX] [Abstract] [Download PDF]
    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.
    @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. doi:10.1109/CoASE.2015.7294035
    [BibTeX] [Abstract] [Download PDF]
    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.
    @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. doi:10.1109/CoASE.2015.7294224
    [BibTeX] [Abstract] [Download PDF]
    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.
    @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. doi:10.1109/EESMS.2015.7175846
    [BibTeX] [Abstract] [Download PDF]
    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.
    @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. doi:10.1016/j.compind.2014.12.004
    [BibTeX] [Abstract] [Download PDF]
    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.
    @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. 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(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. doi:10.1016/j.ifacol.2015.06.113
    [BibTeX] [Abstract] [Download PDF]
    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.
    @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. 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    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. doi:10.1109/CDC.2015.7403147
    [BibTeX] [Abstract] [Download PDF]
    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.
    @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. doi:10.1016/j.ifacol.2015.06.153
    [BibTeX] [Abstract] [Download PDF]
    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.
    @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. doi:10.1145/2809643.2809645
    [BibTeX] [Abstract] [Download PDF]
    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).
    @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. 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(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. doi:10.1007/978-3-319-17509-6_15
    [BibTeX] [Abstract] [Download PDF]
    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.
    @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. doi:10.1016/j.cie.2014.10.026
    [BibTeX] [Abstract] [Download PDF]
    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.
    @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. 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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. doi:10.1016/j.neucom.2013.05.066
    [BibTeX] [Abstract] [Download PDF]
    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.
    @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. doi:10.23919/ecc.2009.7075154
    [BibTeX] [Abstract] [Download PDF]
    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.
    @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},
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