Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/30824
DC FieldValueLanguage
dc.contributor.authorMavrovouniotis, Michalis-
dc.contributor.authorMenelaou, Charalambos-
dc.contributor.authorTimotheou, Stelios-
dc.contributor.authorEllinas, Georgios-
dc.contributor.authorPanayiotou, Christos-
dc.contributor.authorPolycarpou, Marios M.-
dc.date.accessioned2023-11-20T12:00:00Z-
dc.date.available2023-11-20T12:00:00Z-
dc.date.issued2020-07-01-
dc.identifier.citation2020 IEEE Congress on Evolutionary Computation, CEC 2020Virtual, Glasgow, Scotland, 19 -24 July 2020en_US
dc.identifier.isbn9781728169293-
dc.identifier.urihttps://hdl.handle.net/20.500.14279/30824-
dc.description.abstractSevera1 logistic companies started utilizing electric vehicles (EVs) in their daily operations to reduce greenhouse gas pollution. However, the limited driving range of EVs may require visits to recharging stations during their operation. These potential visits have to be addressed, avoiding unnecessary long detours. We formulate the electric capacitated vehicle routing problem (E-CVRP), which incorporates the possibility of EVs visiting a recharging station while satisfying the delivery demands of customers. The energy consumption of the EVs is proportional to their cargo load which is an important constraint in real-world logistics applications. A new set of benchmark instances is proposed for the E-CVRP. As solution methods to these new benchmarks, we apply the ant colony optimization metaheuristic method and an exact method. Experimental results on the ECVRP demonstrate the high complexity of the problem and the efficiency of the applied metaheuristic solution method.en_US
dc.language.isoenen_US
dc.rights© IEEEen_US
dc.subjectbenchmark instanceen_US
dc.subjectcapacitated vehicle routing problemen_US
dc.subjectElectric vehicleen_US
dc.titleA Benchmark Test Suite for the Electric Capacitated Vehicle Routing Problemen_US
dc.typeConference Papersen_US
dc.collaborationUniversity of Cyprusen_US
dc.subject.categoryComputer and Information Sciencesen_US
dc.journalsSubscriptionen_US
dc.countryCyprusen_US
dc.subject.fieldNatural Sciencesen_US
dc.publicationPeer Revieweden_US
dc.relation.conference2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedingsen_US
dc.identifier.doi10.1109/CEC48606.2020.9185753en_US
dc.identifier.scopus2-s2.0-85092044926en
dc.identifier.urlhttps://api.elsevier.com/content/abstract/scopus_id/85092044926en
dc.contributor.orcid#NODATA#en
dc.contributor.orcid#NODATA#en
dc.contributor.orcid#NODATA#en
dc.contributor.orcid#NODATA#en
dc.contributor.orcid#NODATA#en
dc.contributor.orcid#NODATA#en
cut.common.academicyearemptyen_US
item.languageiso639-1en-
item.cerifentitytypePublications-
item.openairetypeconferenceObject-
item.openairecristypehttp://purl.org/coar/resource_type/c_c94f-
item.fulltextNo Fulltext-
item.grantfulltextnone-
crisitem.author.orcid0000-0002-5281-4175-
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