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

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 parameterization 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 computational effort. A comparative analysis with the TOPSIS method further confirms the effectiveness and reliability of the proposed approach for MCDM under uncertainty