Enhanced multi-criteria decision-making through fuzzy soft set parameter reduction and score optimization

Abstract

 Parameter reduction is a crucial task in multi-criteria decision making (MCDM), particularly when dealing with high-dimensional and uncertain data. Fuzzy soft set (FSS) theory, which integrates the param eterization capability of soft sets with the uncertainty-handling strength of fuzzy sets, provides an effective framework for such problems. In this paper, we focus on parameter reduction in FSS and its impact on score-based decision-making. Existing approaches, including S-normal and I-S-normal parameter reduction methods, often lead to identical or indistinguishable decision scores and involve considerable computational complexity. To overcome these limitations, a new and efficient decision making algorithm is proposed within the fuzzy soft set framework, yielding unique and more discriminative scores after parameter reduction. The proposed algorithm is applied to illustrative examples and real-life decision making problems, demonstrating improved accuracy and reduced com putational effort. A comparative analysis with the TOPSIS method further confirms the effectiveness and reliability of the proposed approachfor MCDMunder uncertainty.