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Published in: International Journal of Machine Learning and Cybernetics 8/2020

14-02-2020 | Original Article

A q-rung orthopair fuzzy multi-criteria group decision making method for supplier selection based on a novel distance measure

Authors: Adem Pinar, Fatih Emre Boran

Published in: International Journal of Machine Learning and Cybernetics | Issue 8/2020

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Abstract

Supplier selection and evaluation is a crucial decision-making issue to establish an effective supply chain. Higher-order fuzzy decision-making methods have become powerful tools to support decision-makers in solving their problems effectively by reflecting uncertainty in calculations better than crisp sets in the last 3 decades. The q-rung orthopair fuzzy (q-ROF) sets which are the general form of both intuitionistic and Pythagorean fuzzy sets, have been recently introduced to provide decision-makers more freedom of expression than other fuzzy sets. In this paper, we introduce q-ROF TOPSIS and q-ROF ELECTRE as two separate methods and new approaches for group decision making to select the best supplier. As the existing distance measures in q-rung orthopair fuzzy environment have some drawbacks and generate counter-intuitive results, we propose a new distance measure along with its proofs to use in both q-ROF TOPSIS and q-ROF ELECTRE methods. Moreover, a comparison study is conducted to illustrate the superiority of the proposed distance measure. Subsequently, a comprehensive case study is performed with q-ROF TOPSIS and q-ROF ELECTRE methods separately to choose the best supplier for a construction company by rating the importance of criteria and alternatives under q-ROF environment. Finally, a comparison and parameter analysis are performed among the proposed q-ROF TOPSIS and q-ROF ELECTRE methods and existing q-ROF decision-making methods to demonstrate the effectiveness of our proposed methods.

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Metadata
Title
A q-rung orthopair fuzzy multi-criteria group decision making method for supplier selection based on a novel distance measure
Authors
Adem Pinar
Fatih Emre Boran
Publication date
14-02-2020
Publisher
Springer Berlin Heidelberg
Published in
International Journal of Machine Learning and Cybernetics / Issue 8/2020
Print ISSN: 1868-8071
Electronic ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-020-01070-1

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