Multiple Attribute Sorting via CRITIC-ARASsort Hybrid Method Under Intuitionistic Fuzzy Environment and an Application in Credit Rating

Sorting-based multiple attribute decision-making (MADM) methods address managerial challenges in the hypercompetitive contemporary business life effectively by facilitating efficient organization and retrieval of data, thereby enhancing analysis, optimization, and user experience. Among others, the additive ratio assessment sorting method (ARASsort) is a highly preferable one due to its capacity to offer a dependable sorting mechanism for multi-attribute assessment, characterized by a notable degree of practicality. While experts traditionally assign attribute weights subjectively in sorting methods, robust and objective tools like criteria importance through intercriteria correlation (CRITIC) offer scientific approaches to balance subjectivity. The originality of this study is threefold: First of all, this study is the first one proposing a CRITIC-ARASsort hybrid algorithm to mitigate subjectivity in sorting. Secondly, to address uncertainty in human judgment more comprehensively, it introduces the integration of fuzzy logic, particularly intuitionistic fuzzy sets (IFSs), into ARASsort and thus to the hybrid algorithm, resulting in the development of IF-CRITIC-ARASsort. Furthermore, a new intuitionistic fuzzy standard deviation formula is proposed to overcome the early defuzzification problem in previously proposed versions of IF-CRITIC method in the literature. The applicability of this method is demonstrated in a credit rating scenario, showcasing its utility in complex decision-making processes.