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Erschienen in: Soft Computing 3/2020

10.05.2019 | Methodologies and Application

Intuitionistic Fuzzy TOPSIS method for green supplier selection problem

verfasst von: Babak Daneshvar Rouyendegh, Abdullah Yildizbasi, Pelin Üstünyer

Erschienen in: Soft Computing | Ausgabe 3/2020

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Abstract

One of the most important functions of supply chain management is to enhance competitive pressure. Competition conditions and customer perception have changed in favor of environmentalist attitude. Therefore, green supplier selection (GSS) has become an important issue. In this study, the problem of GSS aiming for lean, agile, environmentally sensitive, sustainability, and durability is addressed. The environmental criteria considered in GSS and classical supplier selection are different from each other in terms of carbon footprint, water consumption, environmental applications, and recycling applications. The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method has been used in the problem of GSS by considering the multi-criteria decision-making (MCDM) method since MCDM is very effective in many aspects such as evaluating and selecting the classical and environmental criteria. Due to linguistic criteria and no possibility to measure all criteria, it is needed to consolidate the fuzzy approach with the TOPSIS method to reduce the effects of ambiguity and instability. The Intuitionistic Fuzzy TOPSIS method is used because this method makes evaluating decision-makers and criteria convenient. According to the criteria determined by the order of importance, the hybrid method resulting from combining the Intuitionistic Fuzzy Set and TOPSIS is very effective to select which supplier is more suitable among the alternatives and also this method can be integrated to similar problems.

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Metadaten
Titel
Intuitionistic Fuzzy TOPSIS method for green supplier selection problem
verfasst von
Babak Daneshvar Rouyendegh
Abdullah Yildizbasi
Pelin Üstünyer
Publikationsdatum
10.05.2019
Verlag
Springer Berlin Heidelberg
Erschienen in
Soft Computing / Ausgabe 3/2020
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-019-04054-8

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