Decision making under measure-based granular uncertainty with intuitionistic fuzzy sets

Abstract

Yager has proposed the decision making under measure-based granular uncertainty, which can make decision with the aid of Choquet integral, measure and representative payoffs. The decision making under measure-based granular uncertainty is an effective tool to deal with uncertain issues. The intuitionistic fuzzy environment is the more real environment. Since the decision making under measure-based granular uncertainty is not based on intuitionistic fuzzy environment, it cannot effectively solve the decision issues in the intuitionistic fuzzy environment. Then, when the issues of decision making are under intuitionistic fuzzy environment, what is the decision making under measure-based granular uncertainty with intuitionistic fuzzy sets is still an open issue. To deal with this kind of issues, this paper proposes the decision making under measure-based granular uncertainty with intuitionistic fuzzy sets. The decision making under measure-based granular uncertainty with intuitionistic fuzzy sets can effectively solve the decision making issues in the intuitionistic fuzzy environment, in other words, it can extend the decision making under measure-based granular uncertainty to the intuitionistic fuzzy environment. Numerical examples are applied to verify the validity of the decision making under measure-based granular uncertainty with intuitionistic fuzzy sets. The experimental results demonstrate that the decision making under measure-based granular uncertainty with intuitionistic fuzzy sets can represent the objects successfully and make decision effectively. In addition, a practical application of applied intelligence is used to compare the performance between the proposed model and the decision making under measure-based granular uncertainty. The experimental results show that the proposed model can solve some decision problems that the decision making under measure-based granular uncertainty cannot solve.

T-Spherical Fuzzy Frank Aggregation Operators and Their Application to Decision Making With Unknown Weight Information

“…4). T-spherical fuzzy Frank operators can tackle the problems considered in the existing literature [24], [42], [43], but the existing operators of IFSs, PyFSs, PFSs and q-ROFSs cannot address the problems described in T-spherical fuzzy environment.…”

International entrepreneurial startups' location under uncertainty through a heterogeneous multi-layer decision-making approach: evidence and application of an emerging economy

“…IFSs are also applied extensively in decision-making problems (Afful-Dadzie et al, 2017). IFSs applications in MCDM continued in several newer studies (Xue and Deng, 2021;Ejegwa and Onyeke, 2021).…”

A theoretical development of improved cosine similarity measure for interval valued intuitionistic fuzzy sets and its applications

“…A measure of similarity is among the ideal tools for identifying a pattern in many disciplines where the optimum decision or optimality criteria have been specified (Singh and Ganie 2021 ). Due to its extensive potential applications in many sectors, including machine learning, medical diagnosis, medical image processing, decision-making, pattern recognition, and so on (Nguyen 2021 ; Xue and Deng 2021 ), there has been fast progress in the investigation of similarity measures in recent decades. Through the analysis of fuzzy sets and their expansions (Chen and Liu 2022 ; Gohain et al.…”