A Cutting-Edge TOPSIS Approach for Navigating MCDM Challenges Under t-Intuitionistic Fuzzy Environments

The significance of t-intuitionistic fuzzy TOPSIS lies in its ability to address challenges related to uncertainty and ambiguity within the decision-making process. The incorporation of the “t” parameter as a t-norm and t-conorm operator offers a more comprehensive and precise approach, making it essential in scenarios where standard intuitionistic fuzzy TOPSIS approaches are insufficient. The implementation of this methodology across multiple domains enhances the dependability and robustness of decision-making procedures. The classification of the technique as an indispensable tool with a broad spectrum of applications is substantially bolstered by its fundamental qualities of versatility, adaptability, and flexibility. In this study, we introduce a novel distance measure called the lift-distance measure between t-intuitionistic fuzzy sets and examines its structural properties. Then, the superiority of this new distance measure is compared with some existing distance measures. To address circumstances with inherent ambiguity, we present a novel decision-making tool called the t-intuitionistic fuzzy TOPSIS technique based on the proposed distance measure. The integration of t-intuitionistic fuzzy terminology into this approach augments the versatility and inclusiveness of the TOPSIS methodology. The case analysis demonstrates that the developed strategy’s effectiveness and precision are superior to those of established alternatives. By providing a flexible and all-encompassing tool for decision-making in conditional environments, the application of this methodology possesses the capacity to generate significant favorable results. In addition, a comparative analysis is undertaken, which includes established TOPSIS methods, to demonstrate the improved performance of the proposed method. The comparison results demonstrate the ability of the proposed method to effectively capture the imprecision or vague differences in t-intuitionistic fuzzy sets so as to obtain more accurate and reliable ranking results.