Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/30864
DC FieldValueLanguage
dc.contributor.authorMavrovouniotis, Michalis-
dc.contributor.authorYang, Shengxiang-
dc.date.accessioned2023-11-27T12:43:12Z-
dc.date.available2023-11-27T12:43:12Z-
dc.date.issued2013-01-01-
dc.identifier.citationStudies in Computational Intelligence, 2013, vol. 505, pp. 283 - 301en_US
dc.identifier.isbn9783642393037-
dc.identifier.issn1860949X-
dc.identifier.urihttps://hdl.handle.net/20.500.14279/30864-
dc.description.abstractOver the years, several variations of the dynamic vehicle routing problem (DVRP) have been considered due to its similarities with many real-world applications. Several methods have been applied to address DVRPs, in which ant colony optimization (ACO) has shown promising results due to its adaptation capabilities. In this chapter, we generate another variation of the DVRP with traffic factor and propose a memetic algorithm based on the ACO framework to address it. Multiple local search operators are used to improve the exploitation capacity and a diversity scheme based on random immigrants is used to improve the exploration capacity of the algorithm. The proposed memetic ACO algorithm is applied on different test cases of the DVRP with traffic factors and is compared with other peer ACO algorithms. The experimental results show that the proposed memetic ACO algorithm shows promising results. © 2013 Springer-Verlag Berlin Heidelberg.en_US
dc.language.isoenen_US
dc.relation.ispartofStudies in Computational Intelligenceen_US
dc.rights© Springer-Verlag Berlin Heidelbergen_US
dc.subjectDynamic vehicle routingen_US
dc.subjectmemetic anten_US
dc.subjectcolony optimization approachen_US
dc.titleDynamic vehicle routing: A memetic ant colony optimization approachen_US
dc.typeArticleen_US
dc.collaborationDe Montfort Universityen_US
dc.subject.categoryComputer and Information Sciencesen_US
dc.journalsSubscriptionen_US
dc.countryUnited Kingdomen_US
dc.subject.fieldNatural Sciencesen_US
dc.publicationPeer Revieweden_US
dc.identifier.doi10.1007/978-3-642-39304-4_11en_US
dc.identifier.scopus2-s2.0-84880403907en
dc.identifier.urlhttps://api.elsevier.com/content/abstract/scopus_id/84880403907en
dc.contributor.orcid#NODATA#en
dc.contributor.orcid#NODATA#en
dc.relation.volume505en_US
cut.common.academicyear2013-2014en_US
dc.identifier.spage283en_US
dc.identifier.epage301en_US
item.grantfulltextnone-
item.languageiso639-1en-
item.cerifentitytypePublications-
item.openairecristypehttp://purl.org/coar/resource_type/c_6501-
item.openairetypearticle-
item.fulltextNo Fulltext-
crisitem.author.orcid0000-0002-5281-4175-
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