A Detailed Analysis of Entropy, Similarity and Distance in q-ROFS for MCDM Applications

Current era witnessed a tremendous development in fuzzy mathematics. Q-rung Orthopair Fuzzy Sets (q-ROFS) assists in real time applications for decision making by providing a way of expressing inaccuracy, ambiguity and other uncertainties. The presented framework broadens the area of Pythagorean and Intuitionistic Fuzzy sets, after providing a more compliant limitation on various membership and non-membership degrees. Through this they become capable in depicting advance and difficult real-world problems. Specifically, where traditional approaches of fuzzy sets fail, q-ROFS expands its area by providing a wider spectrum of human needs and preferences. Within the q-ROFS environment, researchers and practitioners can more precisely estimate the degree of fuzziness in each set by using distance, similarity, and entropy measurements. These steps are basic instruments for creating strong MCDM approaches, helping decision-makers to negotiate complex issues in several fields with more accuracy and dependability.