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

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.

Fire Safety Evaluation for Scenic Spots: An Evidential Best-Worst Method

“…One of the important problems in MCDM is to deal with uncertainty [9,10]. Many mathematical tools such as fuzzy sets [11][12][13][14], neutrosophic sets [15], and Z-numbers [16] are used. For example, Xue et al [17] addressed the uncertain database retrieval problem based on intuitionistic fuzzy set.…”

Similarity Measures of T-Spherical Fuzzy Sets Based on the Cosine Function and Their Applications in Pattern Recognition

“…According to T-spherical fuzzy graphs' notion, two algorithms for solving supply chain management and service center evaluation problems were developed by Guleria and Bajaj [46]. Quek et al [47] proposed a novel method based on generalized T-spherical fuzzy weighted aggregation operators on neutrosophic sets and applied it to multi-attribute decision making problem. Ullah et al [48] developed a multi-attribute decision making method based on the interval-valued T-spherical fuzzy set (IVTSFS) and applied it to policy evaluation.…”

Aggregation operators and VIKOR method based on complex q-rung orthopair uncertain linguistic informations and their applications in multi-attribute decision making

“…In the future, we will evaluate some more aggregation operators (Ullah et al 2018a , 2020 ; Ghorabaee et al 2017 ; Shen and Wang 2018 ), similarity measures (Jana et al 2020a , b ; Liu et al 2020b ; Wang and Zhang 2012 ; Wang et al 2012 ; Zhang et al 2020b , c , d , e ; Zhan et al 2020a , b ; Jiang et al 2020 ), and different methods (He et al 2019 ; Garg et al 2020 ; Ali and Mahmood 2020a , b ; Jan et al 2020 ; Ali et al 2020 ; Mahmood et al 2019 ; Ullah et al 2018b , 2019b ; Quek et al 2019 ) solved by using the complex q-rung orthopair uncertain linguistic information.…”