DC FieldValueLanguage
dc.contributor.authorMatijević, Lukaen_US
dc.date.accessioned2022-11-29T10:26:44Z-
dc.date.available2022-11-29T10:26:44Z-
dc.date.issued2022-
dc.identifier.urihttp://researchrepository.mi.sanu.ac.rs/handle/123456789/4876-
dc.description.abstractThis paper focuses on the Electric Vehicle Routing Problem with soft time windows and time-dependent speeds. The goal is to minimize the total distance traveled and the penalty for arriving early or late at the customers’ locations. We present a Mixed Integer Linear Program (MILP) formulation and propose a General Variable Neighborhood Search (GVNS) metaheuristic as a solution approach. We tested the model and GVNS against the Adaptive Large Neighborhood Search (ALNS) algorithm, using a set of benchmark instances. Experimental evaluation indicates that GVNS can find better quality solutions than MILP and ALNS or the same quality solution in less time.en_US
dc.titleGeneral Variable Neighborhood Search for Electric Vehicle Routing Problemen_US
dc.typeConference Paperen_US
dc.relation.conferenceICVNS Abu Dhabi, U.A.E., October, 25-28, 2022en_US
dc.relation.publicationBook of Abstracten_US
dc.contributor.affiliationComputer Scienceen_US
dc.contributor.affiliationMathematical Institute of the Serbian Academy of Sciences and Arts-
dc.description.rankM34-
item.cerifentitytypePublications-
item.openairetypeConference Paper-
item.fulltextNo Fulltext-
item.grantfulltextnone-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
crisitem.author.orcid0000-0002-4575-6720-
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