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Issue title: Special section: Decision Making Using Intelligent and Fuzzy Techniques
Guest editors: Cengiz Kahraman
Article type: Research Article
Authors: Goker, Nazli | Dursun, Mehtap; * | Albayrak, Esra
Affiliations: Deparment of Industrial Engineering, Galatasaray University, Ortakoy, Istanbul, Turkey
Correspondence: [*] Corresponding author. Mehtap Dursun, Deparment of Industrial Engineering, Galatasaray University, Ortakoy, Istanbul 34349, Turkey. E-mail: [email protected].
Abstract: Supply chain agility is an indispensable way for the companies to quickly response to the demands of the customers. For this reason, agility of supply chain is indispensable in dynamic markets that have high scale of diversity and subjective needs. Supply chain agility needs a systemic procedure that gives priority to feedbacks of customers and follows the changes of competitors in the sector. An efficient supplier evaluation procedure is indispensable for reaching supply chain agility. Agile supplier selection needs to take into account various criteria that incorporate vagueness and uncertainty, obtaining general a multiple level hierarchical system that allows conducting a more efficient decision analysis. Thus, in this paper an integrated fuzzy multi-criteria group decision making procedure based on quantifier-guided ordered weighted average (OWA) method and fuzzy integral, which allows incorporating uncertain data expressed as linguistic terms into the analysis, is proposed for identifying the most suitable agile supplier alternative. In group decision making issues, aggregating experts’opinions is vital to achieve more robust results. As quantifier-guided OWA method is appropriate for decision making problems under uncertain environments, it is employed for the aggregation of experts’evaluations. The developed decision procedure is illustrated via a case study performed in a dye producer in Turkish dye sector.
Keywords: Imprecise data, agile supplier selection, fuzzy measure, quantifier-guided OWA, fuzzy integral
DOI: 10.3233/JIFS-189113
Journal: Journal of Intelligent &Fuzzy Systems, vol. 39, no. 5, pp. 6497-6505, 2020
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