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dc.contributorFaculty of Businessen_US
dc.creatorWang, Wen_US
dc.creatorWang, Sen_US
dc.creatorZhen, Len_US
dc.creatorLaporte, Gen_US
dc.date.accessioned2024-06-04T07:39:56Z-
dc.date.available2024-06-04T07:39:56Z-
dc.identifier.issn0191-2615en_US
dc.identifier.urihttp://hdl.handle.net/10397/106815-
dc.language.isoenen_US
dc.publisherElsevier Ltden_US
dc.rights© 2023 Elsevier Ltd. All rights reserved.en_US
dc.rights© 2023. 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 Wang, W., Wang, S., Zhen, L., & Laporte, G. (2023). The impact of autonomous ships in regional waterways. Transportation Research Part B: Methodological, 178, 102851 is available at https://doi.org/10.1016/j.trb.2023.102851.en_US
dc.subjectAutonomous shipen_US
dc.subjectBenders decompositionen_US
dc.subjectBranch-and-cuten_US
dc.subjectSample average approximationen_US
dc.subjectShipping company operationsen_US
dc.titleThe impact of autonomous ships in regional waterwaysen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.volume178en_US
dc.identifier.doi10.1016/j.trb.2023.102851en_US
dcterms.abstractTechnological innovation has been reshaping all walks of life, and the marine shipping industry is no exception. Autonomous vessels have gained significant attention due to their numerous advantages. However, regulatory constraints and expensive manufacturing costs are impeding the application of autonomous vessels. To overcome these challenges, this research conducts experiments with autonomous ships on national waterways with less regulation and develops a model to investigate their impact on shipping company operations. The model simultaneously optimizes ship routing, fleet sizing, fleet deployment, and demand fulfillment, taking into account demand uncertainty. Two solution methods, i.e., sample average approximation and a two-phase Benders-based branch-and-cut algorithm, are proposed to solve the problem with acceleration strategies, including column generation and variable fixing. The performance of several solution techniques is tested through numerical experiments using real-world data. Besides, sensitivity analyses are conducted to further discuss the influence of key factors and derive constructive managerial insights for shipping companies.en_US
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationTransportation research. Part B, Methodological, Dec. 2023, v. 178, 102851en_US
dcterms.isPartOfTransportation research. Part B, Methodologicalen_US
dcterms.issued2023-12-
dc.identifier.scopus2-s2.0-85175338708-
dc.identifier.eissn1879-2367en_US
dc.identifier.artn102851en_US
dc.description.validate202406 bcchen_US
dc.description.oaAccepted Manuscripten_US
dc.identifier.FolderNumbera2751-
dc.identifier.SubFormID48231-
dc.description.fundingSourceRGCen_US
dc.description.fundingSourceOthersen_US
dc.description.fundingTextNational Natural Science Foundation of Chinaen_US
dc.description.pubStatusPublisheden_US
dc.description.oaCategoryGreen (AAM)en_US
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