فهرست نشریات علمی دسترسی آزاد ایران

Ranking alternatives in Multi-Criteria Decision-Making (MCDM) often involves uncertainty that classical approaches cannot adequately manage. Triangular Interval Type-2 Fuzzy (TriIT2F) numbers provide richer representations of vagueness, but existing ranking techniques remain constrained. Wang’s method applies only to limited cases, Maji’s approach requires identical cores, and Gong, as well as Javanmard and Nehi, suffer from weak sensitivity and limited discriminatory capability. To overcome these shortcomings, this study introduces a generalizable and weighted ranking framework for TriIT2F numbers. By enabling adjustable weights between lower and  Upper Membership Functions (UMFs), the method captures various decision-maker attitudes while preserving computational simplicity. Its linear structure further allows seamless integration with the Simple Additive Weighting (SAW) method.Comparative experiments and sensitivity analyses confirmed the method’s ability to deliver more precise and stable rankings than existing approaches. A case study on cricket player selection validates its practical utility, demonstrating superiority in discrimination, generalizability, and interpretability. Overall, the proposed framework fills a clear theoretical gap and offers strong potential for broader optimization and MCDM applications.