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Erschienen in: Granular Computing 4/2020

22.05.2019 | Original Paper

Supplier selection using a flexible interval-valued fuzzy VIKOR

verfasst von: Iman Mohamad Sharaf

Erschienen in: Granular Computing | Ausgabe 4/2020

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Abstract

One of the major issues in a supply chain (SC) is the selection of the appropriate supplier. Supplier selection (SS) plays a vital role in achieving an effective and successful SC, hence gaining a competitive advantage. This article proposes a novel flexible multi-attribute group decision-making method for SS based on VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) using interval-valued fuzzy sets (IVFSs). The method avoids defuzzification, minimizes the computations, and considers the decision-makers’ optimism level. Avoiding defuzzification keeps the characteristics of (IVFSs) and prevents the loss of information. To minimize the computations, two main modifications are done. While all VIKOR-based techniques use both the best and the worst solutions; the proposed VIKOR uses the best solution only, and the division operations by the difference between two fuzzy sets to compute the separation measures and the index Q are eliminated. Ranking plays a crucial role in VIKOR, since three ranking lists are required. The signed distance is modified and used for ranking due to its simple, few computations. None of the previously VIKOR-based techniques accounted for the decision-makers’ optimism level. Therefore, the signed distance is used in its basic form to keep the α-level explicitly in the ranking formula. Thus, the proposed technique preserves fuzziness, reduces the computations substantially, and allows the participation of the decision-makers through the optimism level. Two examples are solved: one for illustration and the other to compare the results with previously used methods.

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Metadaten
Titel
Supplier selection using a flexible interval-valued fuzzy VIKOR
verfasst von
Iman Mohamad Sharaf
Publikationsdatum
22.05.2019
Verlag
Springer International Publishing
Erschienen in
Granular Computing / Ausgabe 4/2020
Print ISSN: 2364-4966
Elektronische ISSN: 2364-4974
DOI
https://doi.org/10.1007/s41066-019-00169-3

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