A Perfectly Normal IT2 Fuzzy-Based TOPSIS Usi...

A Perfectly Normal IT2 Fuzzy-Based TOPSIS Using α Cuts Uncertainty Representation for MCDM Problems

Author Name : Uduak A. Umoh, Vadivel S. Murugesan, Daniel E. Asuquo, Udoinyang G. Inyang, Aloysius H. Sequeira

ABSTRACT This paper presents an enhanced multi-criteria decision-making (MCDM) approach that combines a Perfectly Normal Interval Type-2 (PNIT2) Fuzzy Logic with the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), using α-cuts to handle uncertainty in decision-making. A perfectly normal IT2 fuzzy set enables precise uncertainty modeling by ensuring full membership in both upper and lower functions, enhancing α-cut comparisons.The α-cuts provide flexibility, giving decision-makers clearer insights into varying confidence levels. Compared to traditional TOPSIS or IT2F methods, the proposed PNIT2F-TOPSIS model with α-cuts addresses a wider range of uncertainties, leading to more reliable outcomes.