Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/109246
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dc.contributorDepartment of Industrial and Systems Engineering-
dc.creatorZhang, Q-
dc.creatorChan, FTS-
dc.creatorFu, X-
dc.date.accessioned2024-10-03T08:17:25Z-
dc.date.available2024-10-03T08:17:25Z-
dc.identifier.issn0197-6729-
dc.identifier.urihttp://hdl.handle.net/10397/109246-
dc.language.isoenen_US
dc.publisherJohn Wiley & Sons, Inc.en_US
dc.rightsCopyright © 2023 Qing Zhang et al. Tis is an open access article distributed under the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.en_US
dc.rightsThe following publication Zhang, Qing, Chan, Felix T. S., Fu, Xiaowen, Improved Ant Colony Optimization for the Operational Aircraft Maintenance Routing Problem with Cruise Speed Control, Journal of Advanced Transportation, 2023, 8390619, 18 pages, 2023 is available at https://doi.org/10.1155/2023/8390619.en_US
dc.titleImproved ant colony optimization for the operational aircraft maintenance routing problem with cruise speed controlen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.volume2023-
dc.identifier.issue1-
dc.identifier.doi10.1155/2023/8390619-
dcterms.abstractThe operational aircraft maintenance routing problem (OAMRP) plays a critical part in producing considerable cost reductions for airlines, since its solution directly influences the number of operating leased aircraft. To reduce the quantity of required aircraft, adopting cruise speed control in OAMRP is a good strategy. In this paper, we investigate the OAMRP with cruise speed control. The objective is to minimize the required quantity of aircraft by finding the optimal aircraft routes through cruise time optimization. The focus is on solving two issues simultaneously: (i) optimization of cruise times and (ii) determination of aircraft routes. Since the combination of two intricate sets of decisions poses significant methodological challenges, the difficulty lies in how to efficiently solve it. Accordingly, the goal of this study is twofold: (i) to design a preprocessing step to reduce the network size and (ii) to develop an improved ant colony optimization (IACO) algorithm with a new state transition mechanism to provide the guidance for cruise times optimization and a new pheromone updating mechanism to enhance the search efficiency and precision. Using data from the Bureau of Transportation Statistics (BTS), we demonstrate the computational efficiency of the preprocessing step and the IACO algorithm.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationJournal of advanced transportation, 2023, v. 2023, no. 1, 8390619-
dcterms.isPartOfJournal of advanced transportation-
dcterms.issued2023-
dc.identifier.scopus2-s2.0-85160635147-
dc.identifier.eissn2042-3195-
dc.identifier.artn8390619-
dc.description.validate202410 bcch-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_Scopus/WOSen_US
dc.description.fundingSourceOthersen_US
dc.description.fundingTextMacau University of Science and Technology Faculty Research Grants (FRG); Te Macau FoundationFund (MFP)en_US
dc.description.pubStatusPublisheden_US
dc.description.oaCategoryCCen_US
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