Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/104573
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dc.contributorDepartment of Industrial and Systems Engineeringen_US
dc.creatorZhang, Xen_US
dc.creatorDeng, Yen_US
dc.creatorChan, FTSen_US
dc.creatorAdamatzky, Aen_US
dc.creatorMahadevan, Sen_US
dc.date.accessioned2024-02-05T08:51:14Z-
dc.date.available2024-02-05T08:51:14Z-
dc.identifier.issn0954-4054en_US
dc.identifier.urihttp://hdl.handle.net/10397/104573-
dc.language.isoenen_US
dc.publisherSage Publications Ltd.en_US
dc.rightsThis is the accepted version of the publication Zhang, X., Deng, Y., Chan, F. T. S., Adamatzky, A., & Mahadevan, S. (2016). Supplier selection based on evidence theory and analytic network process. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 230(3), 562–573. © IMechE 2014. DOI: 10.1177/0954405414551105.en_US
dc.subjectAnalytical network processen_US
dc.subjectDempster'Shafer evidence theoryen_US
dc.subjectDempster'Shafer/analytic network processen_US
dc.subjectSupplier selectionen_US
dc.titleSupplier selection based on evidence theory and analytic network processen_US
dc.typeConference Paperen_US
dc.identifier.spage562en_US
dc.identifier.epage573en_US
dc.identifier.volume230en_US
dc.identifier.issue3en_US
dc.identifier.doi10.1177/0954405414551105en_US
dcterms.abstractThe supplier selection is a key component of the supply chain management. Existing methods for the supplier selection are based on analytic network process. They can handle the interdependence of decision attributes; however, these methods could not guarantee an optimal solution when given vague or incomplete input data. To deal with the uncertainties of input data, we propose methods combining analytic network process with Dempster–Shafer evidence theory. We demonstrate efficiency and accuracy of the proposed method in a numerical example. We demonstrate that the proposed method is flexible and effective in dealing with the supplier selection problem.en_US
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationProceedings of the Institution of Mechanical Engineers. Part B, Journal of engineering manufacture, Mar. 2016, v. 230, no. 3, p. 562-573en_US
dcterms.isPartOfProceedings of the Institution of Mechanical Engineers. Part B, Journal of engineering manufactureen_US
dcterms.issued2016-03-
dc.identifier.scopus2-s2.0-84964058500-
dc.relation.ispartofbookProceedings of the Institution of Mechanical Engineers. Part B, Journal of engineering manufactureen_US
dc.identifier.eissn2041-2975en_US
dc.description.validate202402 bcchen_US
dc.description.oaAccepted Manuscripten_US
dc.identifier.FolderNumberISE-0978-
dc.description.fundingSourceOthersen_US
dc.description.fundingTextNational Natural Science Foundation of China; R&D Program of China; National High Technology Research and Development Program of China; Science and Technology Planning Project of Guangdong Province, China; Business Intelligence Key Team of Guangdong University of Foreign Studies; Key Laboratory of Virtual Reality Technology and Systems, Beihang Universityen_US
dc.description.pubStatusPublisheden_US
dc.identifier.OPUS6637098-
dc.description.oaCategoryGreen (AAM)en_US
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