Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/98255
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dc.contributorDepartment of Logistics and Maritime Studiesen_US
dc.creatorZhen, Len_US
dc.creatorMa, Cen_US
dc.creatorWang, Ken_US
dc.creatorXiao, Len_US
dc.creatorZhang, Wen_US
dc.date.accessioned2023-04-27T01:04:17Z-
dc.date.available2023-04-27T01:04:17Z-
dc.identifier.issn1366-5545en_US
dc.identifier.urihttp://hdl.handle.net/10397/98255-
dc.language.isoenen_US
dc.publisherPergamon Pressen_US
dc.rights© 2020 Elsevier Ltd. All rights reserved.en_US
dc.rights© 2020. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/.en_US
dc.rightsThe following publication Zhen, L., Ma, C., Wang, K., Xiao, L., & Zhang, W. (2020). Multi-depot multi-trip vehicle routing problem with time windows and release dates. Transportation Research Part E: Logistics and Transportation Review, 135, 101866 is available at https://doi.org/10.1016/j.tre.2020.101866.en_US
dc.subjectMulti-depoten_US
dc.subjectMulti-tripen_US
dc.subjectRelease dateen_US
dc.subjectTime windowen_US
dc.subjectVehicle routingen_US
dc.titleMulti-depot multi-trip vehicle routing problem with time windows and release datesen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.volume135en_US
dc.identifier.doi10.1016/j.tre.2020.101866en_US
dcterms.abstractThis study investigates a multi-depot multi-trip vehicle routing problem with time windows and release dates, which is a practical problem in the last mile distribution operations. This problem aims to design a set of trips for the fleet of vehicles supplied by different depots for minimizing total traveling time. It addresses some realistic considerations, such as the customers’ time windows and the release date of customers’ packages. The problem is formulated as a mixed integer programming model. A hybrid particle swarm optimization algorithm and a hybrid genetic algorithm are developed to solve this problem. Extensive numerical experiments are conducted to validate the effectiveness of the proposed model and the efficiency of the proposed solution methods. The experimental results show that our proposed algorithms can obtain near-optimal solutions for small-scale problem instances, and solve some large-scale instances with up to 200 customers, 20 depots and 40 vehicles in reasonable computation time.en_US
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationTransportation research. Part E, Logistics and transportation review, Mar. 2020, v. 135, 101866en_US
dcterms.isPartOfTransportation research. Part E, Logistics and transportation reviewen_US
dcterms.issued2020-03-
dc.identifier.scopus2-s2.0-85079434235-
dc.identifier.eissn1878-5794en_US
dc.identifier.artn101866en_US
dc.description.validate202304 bckwen_US
dc.description.oaAccepted Manuscripten_US
dc.identifier.FolderNumberLMS-0135-
dc.description.fundingSourceSelf-fundeden_US
dc.description.pubStatusPublisheden_US
dc.identifier.OPUS55189247-
dc.description.oaCategoryGreen (AAM)en_US
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