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dc.contributor.authorMavrovouniotis, Michalisen
dc.contributor.authorEllinas, Georgiosen
dc.contributor.authorPolycarpou, Mariosen
dc.coverage.spatialBengaluru, Indiaen
dc.creatorMavrovouniotis, Michalisen
dc.creatorEllinas, Georgiosen
dc.creatorPolycarpou, Mariosen
dc.description.abstractAnt colony optimization (ACO) algorithms have proved to be powerful tools to solve difficult optimization problems. In this paper, ACO is applied to the electric vehicle routing problem (EVRP). New challenges arise with the consideration of electric vehicles instead of conventional vehicles because their energy level is affected by several uncertain factors. Therefore, a feasible route of an electric vehicle (EV) has to consider visit(s) to recharging station(s) during its daily operation (if needed). A look ahead strategy is incorporated into the proposed ACO for EVRP (ACO-EVRP) that estimates whether at any time EVs have within their range a recharging station. From the simulation results on several benchmark problems it is shown that the proposed ACO-EVRP approach is able to output feasible routes, in terms of energy, for a fleet of EVs.en
dc.source2018 IEEE Symposium Series on Computational Intelligence (SSCI)en
dc.titleAnt Colony optimization for the Electric Vehicle Routing Problemen
dc.description.endingpage1241Πολυτεχνική Σχολή / Faculty of EngineeringΤμήμα Ηλεκτρολόγων Μηχανικών και Μηχανικών Υπολογιστών / Department of Electrical and Computer Engineering
dc.type.uhtypeConference Objecten
dc.contributor.orcidPolycarpou, Marios [0000-0001-6495-9171]
dc.contributor.orcidEllinas, Georgios [0000-0002-3319-7677]
dc.contributor.orcidMavrovouniotis, Michalis [0000-0002-5281-4175]

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