Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/107707
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dc.contributorDepartment of Logistics and Maritime Studiesen_US
dc.creatorHuang, Den_US
dc.creatorWang, Yen_US
dc.creatorJia, Sen_US
dc.creatorLiu, Zen_US
dc.creatorWang, Sen_US
dc.date.accessioned2024-07-09T07:09:57Z-
dc.date.available2024-07-09T07:09:57Z-
dc.identifier.issn1812-8602en_US
dc.identifier.urihttp://hdl.handle.net/10397/107707-
dc.language.isoenen_US
dc.publisherTaylor & Francisen_US
dc.rights© 2022 Hong Kong Society for Transportation Studies Limiteden_US
dc.rightsThis is an Accepted Manuscript of an article published by Taylor & Francis in Transportmetrica A: Transport Science on 24 Jan 2022 (published online), available at: http://www.tandfonline.com/10.1080/23249935.2021.2023690.en_US
dc.subjectBus charging schedulingen_US
dc.subjectElectric busen_US
dc.subjectInteger programen_US
dc.subjectLagrangian relaxationen_US
dc.subjectNonlinear charging functionen_US
dc.titleA Lagrangian relaxation approach for the electric bus charging scheduling optimisation problemen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.volume19en_US
dc.identifier.issue2en_US
dc.identifier.doi10.1080/23249935.2021.2023690en_US
dcterms.abstractThe planning and operational decision-making problems of electric transit systems have received significant attention recently in the process of transport electrification. Given an electrified electric transit system with constructed charging facilities, a coordinated bus charging scheduling strategy can improve the system's operating efficiency by fully utilising available charging resources. This paper proposes a novel optimisation approach for the electric bus charging scheduling problem. To tackle the nonlinear relationship between the amount of energy and the time spent charging, this paper discretizes the decision variables for the charging schedule into time intervals. A linear integer program is formulated with the objective of minimising the system's total charging time. A Lagrangian relaxation-based solution approach is proposed to decompose the model into subproblems with respect to individual vehicles. The results provide a number of insights that can help transit operators design cost-effective electric transit operational plans.en_US
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationTransportmetrica, 2023, v. 19, no. 2, 2023690en_US
dcterms.isPartOfTransportmetricaen_US
dcterms.issued2023-
dc.identifier.scopus2-s2.0-85123677892-
dc.identifier.eissn1944-0987en_US
dc.identifier.artn2023690en_US
dc.description.validate202407 bcwhen_US
dc.description.oaAccepted Manuscripten_US
dc.identifier.FolderNumbera2984-
dc.identifier.SubFormID49030-
dc.description.fundingSourceSelf-fundeden_US
dc.description.pubStatusPublisheden_US
dc.description.oaCategoryGreen (AAM)en_US
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