Advances in Decision Sciences (ADS)

Extension of Classical TOPSIS Method Using Q-Rung Orthopair Triangular Fuzzy Number

Extension of Classical TOPSIS Method Using Q-Rung Orthopair Triangular Fuzzy Number

Title

Extension of Classical TOPSIS Method Using Q-Rung Orthopair Triangular Fuzzy Number

Authors

Abstract

Purpose:As an extension of pythagorean fuzzy sets, the q‐rung orthopair fuzzy sets (q‐ROFS) is proposed by Yager in 2017. The q-ROFS offers a novel calculation form for the loss function and effectively deals with unclear information of multi-attribute decision-making (MADM) problems. The concept of q-rung orthopair fuzzy number (q-ROFN) is introduced to facilitate the use of q-ROFS in 2018. This study proposes a comprehensive q‐rung orthopair triangular fuzzy number (q-ROTFN) which is a special notation of q-ROFN, to cope with supplier selection problems. Design/methodology/approach:A new method is developed in this paper for supplier selection MADM problems in uncertain situations. The proposed technique utilizes experts’ knowledge represented by q‐ROFN. It considers the selection of the most proper supplier taking into account flexibility, quality, price, supplier profile, and delivery criteria. Based on the advantages of q-ROFN, this article proposes an extended fuzzy TOPSIS method that does not require aggregation technology. Findings:To verify the proposed technique, a case study is conducted to evaluate and rank the alternative suppliers for an automotive company. As a result of the outcomes, it is shown that the proposed method is suitable for MADM problems. Originality/value:The main contributions of this paper are as follows:(i) Traditional TOPSIS method has been extended using the q-ROTFN to solve multi-attribute decision problems, (ii) It is shown that aggregation techniques are not needed for q-ROTFN based TOPSIS method, (iii) A novel expert weight calculation technique is proposed.

Keywords

Q-Rung orthopair fuzzy number, TOPSIS, supplier selection, multiple attribute decision-making

Classification-JEL

D7, D81

Pages

163-187

https://doi.org/10.47654/v26y2022i1p163-187

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