Open Access

ARTICLE

Multi-Attribute Group Decision-Making Method under Spherical Fuzzy Bipolar Soft Expert Framework with Its Application

Mohammed M. Ali Al-Shamiri^1,2, Ghous Ali^3,*, Muhammad Zain Ul Abidin³, Arooj Adeel³

1 Department of Mathematics, Faculty of Science and Arts, Mahayl Assir, King Khalid University, Abha, Saudi Arabia
2 Department of Mathematics and Computer, Faculty of Science, Ibb University, Ibb, Yemen
3 Department of Mathematics, Division of Science and Technology, University of Education, Lahore, Pakistan

* Corresponding Author: Ghous Ali. Email:

(This article belongs to the Special Issue: Decision making Modeling, Methods and Applications of Advanced Fuzzy Theory in Engineering and Science)

Computer Modeling in Engineering & Sciences 2023, 137(2), 1891-1936. https://doi.org/10.32604/cmes.2023.027844

Received 17 November 2022; Accepted 14 February 2023; Issue published 26 June 2023

Abstract

Spherical fuzzy soft expert set (SFSES) theory blends the perks of spherical fuzzy sets and group decision-making into a unified approach. It allows solutions to highly complicated uncertainties and ambiguities under the unbiased supervision and group decision-making of multiple experts. However, SFSES theory has some deficiencies such as the inability to interpret and portray the bipolarity of decision-parameters. This work highlights and overcomes these limitations by introducing the novel spherical fuzzy bipolar soft expert sets (SFBSESs) as a powerful hybridization of spherical fuzzy set theory with bipolar soft expert sets (BSESs). Followed by the development of certain set-theoretic operations and properties of the proposed model, important problems, including the selection of non-powered dam (NPD) sites for hydropower conversion are discussed and solved under the proposed approach. These problems mainly focus on the need for an efficient tool capable of considering the bipolarity of parameters, complicated ambiguities, and multiple opinions. Supporting the new approach by a detailed comparative analysis, it is concluded that the proposed model is more comprehensive and reliable for multi-attribute group decisionmaking (MAGDM) than the previous tools, particularly considering the bipolarity of parameters under SFSES environment.

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