Publication Type : Journal Article
Publisher : Applied Soft Computing
Source : Applied Soft Computing, Volume 94, p.106431 (2020)
Url : https://www.sciencedirect.com/science/article/pii/S1568494620303719
Keywords : Evidence-based bayes approximation, Group Decision-Making, Maclaurin symmetric mean, q-rung orthopair fuzzy set, Statistical variance and VIKOR method
Campus : Coimbatore
School : School of Engineering
Department : Computer Science
Year : 2020
Abstract : As a powerful generalization to intuitionistic fuzzy set (IFS), q-rung orthopair fuzzy set (q-ROFS) is proposed by Yager, which can effectively mitigate the weakness of IFS and provide wider space for preference elicitation. Based on the literature analysis on q-ROFS, a comprehensive decision framework for promoting rational decision-making is lacking. Motivated by the superiority of q-ROFS and to circumvent the issue, in this paper, a new decision framework with minimum subjective randomness is proposed under q-ROFS context. Initially, decision makers’ (DMs’) relative importance is systematically calculated by extending evidence-based Bayes approximation to q-ROFS. Later, a new operator is proposed for aggregating DMs’ preferences by extending generalized Maclaurin symmetric mean (GMSM) to q-ROFS context. Attributes’ weight values are calculated by using newly proposed q-rung orthopair fuzzy statistical variance (q-ROFSV) method and objects are prioritized by extending the popular VIKOR method to q-ROFS context. Finally, the practical use of the proposed decision framework is validated by using a green supplier selection problem and the strengths and weaknesses of the framework are discussed by using comparative analysis with other methods.
Cite this Research Publication : R. Krishankumar, Y. Gowtham, Ifjaz Ahmed, K. S. Ravichandran, and Samarjit Kar, “Solving green supplier selection problem using q-rung orthopair fuzzy-based decision framework with unknown weight information”, Applied Soft Computing, vol. 94, p. 106431, 2020.