Novel Intuitionistic Fuzzy Decision Making Method for Mobile Selection Problems Based on Grouping and Overlap Function

In decision-making problems, intuitionistic fuzzy sets (IFSs) have proven to be an effective tool within fuzzy set theory. The concepts of grouping and overlap functions play a crucial role in identifying and analyzing commonalities among overlapping criteria or alternatives, thereby enhancing the robustness of decision-making models in complex environments. The main purpose of this study is to establish the basic operational laws and theorems of the Intuitionistic Fuzzy Grouping and Overlap Function (GOF), which provide an effective way to handle data ambiguity. Furthermore, this paper develops two main families of aggregation operators, namely the weighted average family and weighted geometric family. These families consist of four specific operator : the Group and Overlap Function-based Intuitionistic Fuzzy Weighted Average (GOF-IFWA) and the Group and Overlap Function-based Intuitionistic Fuzzy Weighted Geometric (GOF-IFWG), along with their mathematical properties and potential applications. By offering a more adaptable aggregate framework for simulating intricate interdependencies across criteria, the suggested operators lessen the drawbacks seen in purely associative t-norm- and t-conorm-based formulations. The results, which are backed by thorough theoretical assessment and empirical support on mobile selection issues, demonstrate that the suggested method more successfully captures reluctance and ambiguity while maintaining computing efficiency. The efficacy and practical usability of the suggested framework are further confirmed by a comparative evaluation against twenty-seven current approaches