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Open AccessArticle

A New Approach to Fuzzy TOPSIS Method Based on Entropy Measure under Spherical Fuzzy Information

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Faculty of Computing and Information Technology, P.O. Box 411, King Abdulaziz University, Rabigh 21911, Jeddah, Saudi Arabia
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Faculty of Physical and Numerical Sciences, Abdul Wali Khan University, Mardan 23200, Pakistan
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Author to whom correspondence should be addressed.
Entropy 2019, 21(12), 1231; https://doi.org/10.3390/e21121231
Received: 15 November 2019 / Revised: 11 December 2019 / Accepted: 12 December 2019 / Published: 16 December 2019
(This article belongs to the Section Multidisciplinary Applications)
Spherical fuzzy set (SFS) is one of the most important and extensive concept to accommodate more uncertainties than existing fuzzy set structures. In this article, we will describe a novel enhanced TOPSIS-based procedure for tackling multi attribute group decision making (MAGDM) issues under spherical fuzzy setting, in which the weights of both decision-makers (DMs) and criteria are totally unknown. First, we study the notion of SFSs, the score and accuracy functions of SFSs and their basic operating laws. In addition, defined the generalized distance measure for SFSs based on spherical fuzzy entropy measure to compute the unknown weights information. Secondly, the spherical fuzzy information-based decision-making technique for MAGDM is presented. Lastly, an illustrative example is delivered with robot selection to reveal the efficiency of the proposed spherical fuzzy decision support approach, along with the discussion of comparative results, to prove that their results are feasible and credible. View Full-Text
Keywords: spherical fuzzy set; spherical fuzzy entropy measure; extended TOPSIS method; group decision making problems spherical fuzzy set; spherical fuzzy entropy measure; extended TOPSIS method; group decision making problems
MDPI and ACS Style

Barukab, O.; Abdullah, S.; Ashraf, S.; Arif, M.; Khan, S.A. A New Approach to Fuzzy TOPSIS Method Based on Entropy Measure under Spherical Fuzzy Information. Entropy 2019, 21, 1231.

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