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Issue title: Special Section: FLINS 2018
Guest editors: Cengiz Kahraman
Article type: Research Article
Authors: Goker, Nazli | Dursun, Mehtap; * | Cedolin, Michele
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: Companies should cope with uncertain and unpredictable changes while improving responsiveness capability to survive in dynamic market conditions. In recent years, agility concept becomes more and more popular to meet these requirements in management as well as manufacturing. From supply chain viewpoint, collaborating with agile suppliers is principal for constructing an agile supply chain, which is flexible, quick, and responsive. This paper aims to identify the most suitable agile supplier alternative by introducing a novel distance based hierarchical intuitionistic decision making procedure. The proposed methodology, which deals with uncertain and vague data, allows to represent hesitation by using intuitionistic fuzzy numbers rather than fuzzy numbers that fail to take into account hesitancy. The causal links among evaluation criteria are expressed with intuitionistic fuzzy cognitive map tool that enables to yield the importance degree of each criterion. Moreover, the hierarchical representation of the decision making problem provides an effective analysis with the presence of numerous criteria that are to be considered. The distance based framework is to minimize the distance to the ideal solution while maximizing the distance from the anti-ideal solution in order to determine the most appropriate agile supplier alternative. The case study is conducted in a dye manufacturer that performs in Turkish dye industry.
Keywords: Intuitionistic fuzzy sets, intuitionistic fuzzy cognitive map, hierarchical decision making, agile supplier selection
DOI: 10.3233/JIFS-179438
Journal: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 1, pp. 653-662, 2020
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