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Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/30810
DC FieldValueLanguage
dc.contributor.authorMavrovouniotis, Michalis-
dc.contributor.authorEllinas, Georgios-
dc.contributor.authorLi, Changhe-
dc.contributor.authorPolycarpou, Marios M.-
dc.date.accessioned2023-11-16T05:48:11Z-
dc.date.available2023-11-16T05:48:11Z-
dc.date.issued2022-12-04-
dc.identifier.citation2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022, Singapore, Asia, 4 - 7 December 2022en_US
dc.identifier.isbn9781665487689-
dc.identifier.urihttps://hdl.handle.net/20.500.14279/30810-
dc.description.abstractAnt colony optimization (ACO) has been found to be useful on several vehicle routing problem variations. In this work, ACO is applied to the electric vehicle routing problem with time windows (E-VRPTW). The E-VRPTW has a hierarchical multiple objective function, which is to minimize the number of electric vehicles and the total distance traveled. A multiple ACO is applied to E-VRPTW in which two colonies cooperate to minimize the objectives in parallel. A local search is embedded in ACO to improve the quality of the output. The experimental results on a set of benchmark instances show that the multiple ACO is competitive with existing methods.en_US
dc.language.isoenen_US
dc.rights© IEEEen_US
dc.subjectant colony optimizationen_US
dc.subjectElectric vehicleen_US
dc.subjectvehicle routing problem with time windowsen_US
dc.titleA Multiple Ant Colony System for the Electric Vehicle Routing Problem with Time Windowsen_US
dc.typeConference Papersen_US
dc.collaborationUniversity of Cyprusen_US
dc.collaborationChina University of Geosciencesen_US
dc.subject.categoryElectrical Engineering - Electronic Engineering - Information Engineeringen_US
dc.countryCyprusen_US
dc.countryChinaen_US
dc.subject.fieldEngineering and Technologyen_US
dc.relation.conferenceProceedings of the 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022en_US
dc.identifier.doi10.1109/SSCI51031.2022.10022257en_US
dc.identifier.scopus2-s2.0-85147798653en
dc.identifier.urlhttps://api.elsevier.com/content/abstract/scopus_id/85147798653en
dc.contributor.orcid#NODATA#en
dc.contributor.orcid#NODATA#en
dc.contributor.orcid#NODATA#en
dc.contributor.orcid#NODATA#en
cut.common.academicyear2021-2022en_US
item.grantfulltextnone-
item.openairecristypehttp://purl.org/coar/resource_type/c_c94f-
item.fulltextNo Fulltext-
item.languageiso639-1en-
item.cerifentitytypePublications-
item.openairetypeconferenceObject-
crisitem.author.orcid0000-0002-5281-4175-
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