Multi-Attribute Multi-Perception Decision-Making Based on Generalized T-Spherical Fuzzy Weighted Aggregation Operators on Neutrosophic Sets

Shio Gai Quek, Ganeshsree Selvachandran, Muhammad Munir, Tahir Mahmood, Kifayat Ullah, Le Hoang Son, Pham Huy Thong, Raghvendra Kumar and Ishaani Priyadarshini
Additional contact information
Shio Gai Quek: A-Level Academy, UCSI College KL Campus, Lot 12734, Jalan Choo Lip Kung, Taman Taynton View, Cheras, Kuala Lumpur 56000, Malaysia
Ganeshsree Selvachandran: Department of Actuarial Science and Applied Statistics, Faculty of Business and Information Science, UCSI University, Jalan Menara Gading, Cheras, Kuala Lumpur 56000, Malaysia
Muhammad Munir: Department of Mathematics and Statistics, International Islamic University Islamabad, Islamabad 44000, Pakistan
Tahir Mahmood: Department of Mathematics and Statistics, International Islamic University Islamabad, Islamabad 44000, Pakistan
Kifayat Ullah: Department of Mathematics and Statistics, International Islamic University Islamabad, Islamabad 44000, Pakistan
Le Hoang Son: Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
Pham Huy Thong: Division of Data Science, Ton Duc Thang University, Ho Chi Minh City, Vietnam
Raghvendra Kumar: Department of Computer Science and Engineering, LNCT College, Madhya Pradesh 462021, India
Ishaani Priyadarshini: University of Delaware, Newark, DE 19716, USA

Mathematics, 2019, vol. 7, issue 9, 1-34

Abstract: The framework of the T-spherical fuzzy set is a recent development in fuzzy set theory that can describe imprecise events using four types of membership grades with no restrictions. The purpose of this manuscript is to point out the limitations of the existing intuitionistic fuzzy Einstein averaging and geometric operators and to develop some improved Einstein aggregation operators. To do so, first some new operational laws were developed for T-spherical fuzzy sets and their properties were investigated. Based on these new operations, two types of Einstein aggregation operators are proposed namely the Einstein interactive averaging aggregation operators and the Einstein interactive geometric aggregation operators. The properties of the newly developed aggregation operators were then investigated and verified. The T-spherical fuzzy aggregation operators were then applied to a multi-attribute decision making (MADM) problem related to the degree of pollution of five major cities in China. Actual datasets sourced from the UCI Machine Learning Repository were used for this purpose. A detailed study was done to determine the most and least polluted city for different perceptions for different situations. Several compliance tests were then outlined to test and verify the accuracy of the results obtained via our proposed decision-making algorithm. It was proved that the results obtained via our proposed decision-making algorithm was fully compliant with all the tests that were outlined, thereby confirming the accuracy of the results obtained via our proposed method.

Keywords: T-spherical fuzzy sets; single-valued neutrosophic sets; multi-attribute decision making; aggregation operator (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2019
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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