Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/104104
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dc.contributorDepartment of Industrial and Systems Engineering-
dc.creatorEltoukhy, AEEen_US
dc.creatorWang, ZXen_US
dc.creatorChan, FTSen_US
dc.creatorFu, Xen_US
dc.date.accessioned2024-02-05T08:46:18Z-
dc.date.available2024-02-05T08:46:18Z-
dc.identifier.issn1366-5545en_US
dc.identifier.urihttp://hdl.handle.net/10397/104104-
dc.language.isoenen_US
dc.publisherElsevier Ltden_US
dc.rights© 2018 Elsevier Ltd. All rights reserveden_US
dc.rights© 2018. 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 Eltoukhy, A. E. E., Wang, Z. X., Chan, F. T. S., & Fu, X. (2019). Data analytics in managing aircraft routing and maintenance staffing with price competition by a Stackelberg-Nash game model. Transportation Research Part E: Logistics and Transportation Review, 122, 143–168 is available at https://doi.org/10.1016/j.tre.2018.12.002.en_US
dc.subjectAircraft routing problemen_US
dc.subjectData analyticsen_US
dc.subjectGame theoryen_US
dc.subjectMaintenance staffing problemen_US
dc.titleData analytics in managing aircraft routing and maintenance staffing with price competition by a Stackelberg-Nash game modelen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage143en_US
dc.identifier.epage168en_US
dc.identifier.volume122en_US
dc.identifier.doi10.1016/j.tre.2018.12.002en_US
dcterms.abstractThis study develops a Stackelberg-Nash game model (SNGM) to capture the interdependence between aircraft routing of airlines and maintenance staffing of maintenance providers, and to consider the price competition among maintenance providers. The SNGM’s overall Nash equilibrium is obtained using an iterative game algorithm. The SNGM effectiveness is demonstrated with a case study, in which a neural network-based algorithm is developed to forecast accurate non-propagated delays, and a multiple linear regression algorithm is adopted to predict demand-price relationship for each maintenance provider. The results reveal cost savings of about 26% and 22% for the airline and the maintenance providers, respectively.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationTransportation research. Part E, Logistics and transportation review, Feb. 2019, v. 122, p. 143-168en_US
dcterms.isPartOfTransportation research. Part E, Logistics and transportation reviewen_US
dcterms.issued2019-02-
dc.identifier.scopus2-s2.0-85058064643-
dc.identifier.eissn1878-5794en_US
dc.description.validate202402 bcch-
dc.description.oaAccepted Manuscripten_US
dc.identifier.FolderNumberISE-0526-
dc.description.fundingSourceRGCen_US
dc.description.fundingSourceOthersen_US
dc.description.fundingTextThe Natural Science Foundation of China; The Research Committee of Hong Kong Polytechnic Universityen_US
dc.description.pubStatusPublisheden_US
dc.identifier.OPUS14424183-
dc.description.oaCategoryGreen (AAM)en_US
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