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dc.contributor.authorUlutaş, Alptekin
dc.contributor.authorBalo, Figen
dc.contributor.authorSua, Lutfu
dc.contributor.authorDemir, Ezgi
dc.contributor.authorTopal, Ayşe
dc.contributor.authorJakovljević, Vladimir
dc.date.accessioned2021-11-04T08:58:23Z
dc.date.available2021-11-04T08:58:23Z
dc.date.issued2021en_US
dc.identifier.citationUlutaş, A., Balo, F., Sua, L., Demir, E., Topal, A., & Jakovljević, V. (2021). A new integrated grey mcdm model: case of warehouse location selection. Facta Universitatis, Series: Mechanical Engineering, 19(3), p. 515-535.en_US
dc.identifier.issn0354-2025
dc.identifier.urihttps://hdl.handle.net/20.500.12960/1293
dc.description.abstractWarehouses link suppliers and customers throughout the entire supply chain. The location of the warehouse has a significant impact on the logistics process. Even though all other warehouse activities are successful, if the product dispatched from the warehouse fails to meet the customer needs in time, the company may face with the risk of losing customers. This affects the performance of the whole supply chain therefore the choice of warehouse location is an important decision problem. This problem is a multi-criteria decision-making (MCDM) problem since it involves many criteria and alternatives in the selection process. This study proposes an integrated grey MCDM model including grey preference selection index (GPSI) and grey proximity indexed value (GPIV) to determine the most appropriate warehouse location for a supermarket. This study aims to make three contributions to the literature. PSI and PIV methods combined with grey theory will be introduced for the first time in the literature. In addition, GPSI and GPIV methods will be combined and used to select the best warehouse location. In this study, the performances of five warehouse location alternatives were assessed with twelve criteria. Location 4 is found as the best alternative in GPIV. The GPIV results were compared with other grey MCDM methods, and it was found that GPIV method is reliable. It has been determined from the sensitivity analysis that the change in criteria weights causes a change in the ranking of the locations therefore GPIV method was found to be sensitive to the change in criteria weights.en_US
dc.language.isoengen_US
dc.publisherFaculty of Mechanical Engineeringen_US
dc.relation.ispartofFacta Universitatis, Series: Mechanical Engineeringen_US
dc.relation.isversionof10.22190/FUME210424060Uen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectGrey preference selection indexen_US
dc.subjectGrey proximity indexed valueen_US
dc.subjectMulti-criteria decision makingen_US
dc.subjectWarehouse location selectionen_US
dc.titleA new integrated grey mcdm model: Case of warehouse location selectionen_US
dc.typearticleen_US
dc.departmentİktisadi ve İdari Bilimler Fakültesi, Yönetim Bilişim Sistemleri Bölümüen_US
dc.contributor.institutionauthorDemir, Ezgi
dc.identifier.volume19en_US
dc.identifier.issue3en_US
dc.identifier.startpage515en_US
dc.identifier.endpage535en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US


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