CLOSENESS COEFFICIENT RANKING METHOD BASED ON TYPE-2 INTUITIONISTIC FUZZY NUMBERS AND APPLICATION INTO MULTIPLE-CRITERIA GROUP DECISION-MAKING

This paper explores a ranking method for Type-2 Intuitionistic Fuzzy Numbers (T2IFNs). Initially, we discuss the concept of T2IFNs and their operational laws involving addition, multiplication, and exponentiation. Furthermore, we introduce prioritized average operators designed to solve multiple attribute group decision-making (MAGDM) problems under a T2IFN environment, considering varying priority levels for attributes and experts. Specifically, we examine the mathematical properties of the T2IFNs Prioritized Weighted Average (T2IFPWA) operators. Then, after we apply the closeness coefficient method to a normalized prioritized weighted averaging matrix to determine the final ranking of alternatives. To illustrate the feasibility and effectiveness of the proposed approach, a real-world application in the context of talent acquisition is presented. Finally, the alternatives are ranked according to their computed closeness coefficients.