Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/91254
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dc.contributorDepartment of Logistics and Maritime Studiesen_US
dc.creatorLi, Yen_US
dc.creatorLi, Xen_US
dc.creatorShu, Jen_US
dc.creatorSong, Men_US
dc.creatorZhang, Ken_US
dc.date.accessioned2021-10-19T08:27:31Z-
dc.date.available2021-10-19T08:27:31Z-
dc.identifier.issn1091-9856en_US
dc.identifier.urihttp://hdl.handle.net/10397/91254-
dc.language.isoenen_US
dc.publisherInformsen_US
dc.rights© 2021 INFORMSen_US
dc.rightsThis is the accepted manuscript of the following article: Yongzhen Li, Xueping Li, Jia Shu, Miao Song, Kaike Zhang (2021) A General Model and Efficient Algorithms for Reliable Facility Location Problem Under Uncertain Disruptions. INFORMS Journal on Computing 34(1):407-426, which has been published in final form at https://doi.org/10.1287/ijoc.2021.1063.en_US
dc.subjectUncapacitated facility locationen_US
dc.subjectUncertain facility disruptionsen_US
dc.subjectStochastic and distributionally robust optimizationsen_US
dc.subjectCutting planeen_US
dc.subjectColumn generationen_US
dc.titleA general model and efficient algorithms for reliable facility location problem under uncertain disruptionsen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage407en_US
dc.identifier.epage426en_US
dc.identifier.volume34en_US
dc.identifier.issue1en_US
dc.identifier.doi10.1287/ijoc.2021.1063en_US
dcterms.abstractThis paper studies the reliable uncapacitated facility location problem in which facilities are subject to uncertain disruptions. A two-stage distributionally robust model is formulated, which optimizes the facility location decisions so as to minimize the fixed facility location cost and the expected transportation cost of serving customers under the worst-case disruption distribution. The model is formulated in a general form, where the uncertain joint distribution of disruptions is partially characterized and is allowed to have any prespecified dependency structure. This model extends several related models in the literature, including the stochastic one with explicitly given disruption distribution and the robust one with moment information on disruptions. An efficient cutting plane algorithm is proposed to solve this model, where the separation problem is solved respectively by a polynomial-time algorithm in the stochastic case and by a column generation approach in the robust case. Extensive numerical study shows that the proposed cutting plane algorithm not only outperforms the best-known algorithm in the literature for the stochastic problem under independent disruptions but also efficiently solves the robust problem under correlated disruptions. The practical performance of the robust models is verified in a simulation based on historical typhoon data in China. The numerical results further indicate that the robust model with even a small amount of information on disruption correlation can mitigate the conservativeness and improve the location decision significantly.en_US
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationINFORMS journal on computing, Jan.-Feb. 2022, v. 34, no. 1, p. 407-426en_US
dcterms.isPartOfINFORMS journal on computingen_US
dcterms.issued2022-01-
dc.identifier.eissn1526-5528en_US
dc.description.validate202110 bchyen_US
dc.description.oaAccepted Manuscripten_US
dc.identifier.FolderNumbera0792-n01, RGC-B1-019-
dc.identifier.SubFormID1641-
dc.description.fundingSourceRGCen_US
dc.description.fundingSourceOthersen_US
dc.description.fundingTextNSFC, Jiangsu Provincial Six Talent Peaks Project, Jiangsu Province“333”Project, National Science Foundation, Ideation Laboratory (iLab) at the University of Tennessee, Knoxvilleen_US
dc.description.pubStatusPublisheden_US
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