A Hesitant Fuzzy Combined Compromise Solution Framework-Based on Discrimination Measure for Ranking Sustainable Third-Party Reverse Logistic Providers

Arunodaya Raj Mishra, Pratibha Rani, Raghunathan Krishankumar, Edmundas Kazimieras Zavadskas, Fausto Cavallaro and Kattur S. Ravichandran
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Arunodaya Raj Mishra: Department of Mathematics, Government College, Satna, Jaitwara 485221, Madhya Pradesh, India
Pratibha Rani: Department of Mathematics, National Institute of Technology, Warangal 506004, Telangana, India
Raghunathan Krishankumar: Department of Computing, Sastra University, Thanjavur 613401, Tamil Nadu, India
Edmundas Kazimieras Zavadskas: Institute of Sustainable Construction, Vilnius Gediminas Technical University, Sauletekio Ave. 11, LT-10223 Vilnius, Lithuania
Fausto Cavallaro: Department of Economics, University of Molise, Via De Sanctis, 86100 Campobasso, Italy
Kattur S. Ravichandran: Department of Computing, Sastra University, Thanjavur 613401, Tamil Nadu, India

Sustainability, 2021, vol. 13, issue 4, 1-24

Abstract: Customers’ pressure, social responsibility, and government regulations have motivated the enterprises to consider the reverse logistics (RL) in their operations. Recently, companies frequently outsource their RL practices to third-party reverse logistics providers (3PRLPs) to concentrate on their primary concern and diminish costs. However, to select the suitable 3PRLP candidate requires a multi-criteria decision making (MCDM) process involving uncertainty owing to the presence of many associated aspects. In order to choose the most appropriate sustainable 3PRLP (S3PRLP), we introduce a hybrid approach based on the classical Combined Compromise Solution (CoCoSo) method and propose a discrimination measure within the context of hesitant fuzzy sets (HFSs). This approach offers a new process based on the discrimination measure for evaluating the criteria weights. The efficiency and practicability of the present approach are numerically demonstrated by solving an illustrative case study of S3PRLPs selection under a hesitant fuzzy environment. Moreover, sensitivity and comparative studies are presented to highlight the robustness and strength of the introduced methodology. The result of this work concludes that the introduced methodology can recommend a more feasible performance when facing with determinate and inconsistent knowledge and qualitative data.

Keywords: hesitant fuzzy sets; discrimination measure; multi-criteria decision-making; combined compromise solution (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

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