Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/30811
DC FieldValueLanguage
dc.contributor.authorMavrovouniotis, Michalis-
dc.contributor.authorLi, Changhe-
dc.contributor.authorEllinas, Georgios-
dc.contributor.authorPolycarpou, Marios M.-
dc.date.accessioned2023-11-17T06:39:16Z-
dc.date.available2023-11-17T06:39:16Z-
dc.date.issued2022-01-01-
dc.identifier.citation022 IEEE Congress on Evolutionary Computation, CEC 2022, Padua, Italy, 18 - 23 July 2022en_US
dc.identifier.isbn9781665467087-
dc.identifier.urihttps://hdl.handle.net/20.500.14279/30811-
dc.description.abstractElectric vehicle routing problems are challenging variations of the traditional vehicle routing problem which incorporate the possibility of electric vehicle (EV) recharging at any station, while satisfying the delivery demands of customers. This work addresses the recently formulated capacitated vehicle routing problem (E-CVRP) with variable energy consumption rate. In particular, the cargo weight, which is one of the main factors affecting the energy consumption rate of EVs, is considered (i.e., the heavier the EV the higher the rate). As a solution method, an ant colony optimization algorithm with a local search heuristic is developed. Experiments are conducted on a recently generated benchmark set of E-CVRP instances demonstrating that the performance of the proposed technique improves on the best known so far solutions.en_US
dc.language.isoenen_US
dc.rights© IEEEen_US
dc.subjectant colony optimizationen_US
dc.subjectcapacitated vehicle routing problemen_US
dc.subjectElectric vehicleen_US
dc.titleSolving the Electric Capacitated Vehicle Routing Problem with Cargo Weighten_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.conference2022 IEEE Congress on Evolutionary Computation, CEC 2022 - Conference Proceedingsen_US
dc.identifier.doi10.1109/CEC55065.2022.9870383en_US
dc.identifier.scopus2-s2.0-85138725449en
dc.identifier.urlhttps://api.elsevier.com/content/abstract/scopus_id/85138725449en
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.fulltextNo Fulltext-
item.cerifentitytypePublications-
item.grantfulltextnone-
item.openairecristypehttp://purl.org/coar/resource_type/c_c94f-
item.openairetypeconferenceObject-
item.languageiso639-1en-
crisitem.author.orcid0000-0002-5281-4175-
Appears in Collections:Άρθρα/Articles
CORE Recommender
Show simple item record

Page view(s) 20

66
Last Week
0
Last month
10
checked on May 17, 2024

Google ScholarTM

Check

Altmetric


Items in KTISIS are protected by copyright, with all rights reserved, unless otherwise indicated.