Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/91869
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dc.contributorDepartment of Aeronautical and Aviation Engineeringen_US
dc.creatorXu, Gen_US
dc.creatorZhong, Len_US
dc.creatorHu, Xen_US
dc.creatorLiu, Wen_US
dc.date.accessioned2022-01-03T06:00:54Z-
dc.date.available2022-01-03T06:00:54Z-
dc.identifier.issn1366-5545en_US
dc.identifier.urihttp://hdl.handle.net/10397/91869-
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.rights© 2021 Elsevier Ltd. All rights reserved.en_US
dc.rights© 2021. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/en_US
dc.rightsThe following publication Xu, G., Zhong, L., Hu, X., & Liu, W. (2022). Optimal pricing and seat allocation schemes in passenger railway systems. Transportation Research Part E: Logistics and Transportation Review, 157, 102580 is available at https://dx.doi.org/10.1016/j.tre.2021.102580.en_US
dc.subjectPassenger railway systemen_US
dc.subjectPricingen_US
dc.subjectSeat allocationen_US
dc.subjectGlobal optimizationen_US
dc.titleOptimal pricing and seat allocation schemes in passenger railway systemsen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.volume157en_US
dc.identifier.doi10.1016/j.tre.2021.102580en_US
dcterms.abstractThis paper examines optimal pricing and seat allocation schemes in passenger railway systems, where ticket pricing and seat allocation (or capacity allocation) are both Origin-Destination specific. We consider that the demand is sensitive to the ticket price, and a non-concave and non-linear mixed integer optimization model is then formulated for the ticket pricing and seat allocation problem to maximize the railway ticket revenue. To find the optimal solution of the ticket revenue maximization problem effectively, the proposed non-concave and non-linear model is reformulated such that the objective function and constraints are linear with respect to the decision variables or the logarithms of the decision variables. The linearized model is then further relaxed as a mixed-integer programing problem (MILP). Based on the above linearization and relaxation techniques, a globally optimal solution can be obtained by iteratively solving the relaxed MILP and adopting the range reduction scheme. Two numerical examples are presented for illustration.en_US
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationTransportation Research Part E: Logistics and Transportation Review, Jan. 2022, v. 157, 102580en_US
dcterms.isPartOfTransportation research. Part E, Logistics and transportation reviewen_US
dcterms.issued2022-01-
dc.identifier.eissn1878-5794en_US
dc.identifier.artn102580en_US
dc.description.validate202112 bchyen_US
dc.description.oaAccepted Manuscripten_US
dc.identifier.FolderNumbera1132-n01, a1606-
dc.identifier.SubFormID43982, 45602-
dc.description.fundingSourceOthersen_US
dc.description.fundingTextAustralian Research Council (DE200101793)en_US
dc.description.pubStatusPublisheden_US
dc.description.oaCategoryGreen (AAM)en_US
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