A modified fuzzy VIKOR with normalization for supplier selection problem

This study proposes a Modified Fuzzy VIKOR approach that integrates threenormalization techniques (Min-Max, Weight Sum, and Vector) to support multi-criteriadecision-making (MCDM) under uncertainty. It aims to examine the effects of analysismethods (Original vs. Modified VIKOR), data types (Non-Fuzzy vs. Fuzzy),normalization techniques, and v parameters (0.25, 0.5, 0.75) on the resulting set of bestalternatives. A key contribution of this study is the adjustment of the S index to reduceredundancy with R and enhance the discriminative power of Q, leading to more preciserankings. The methodology is applied to two case studies are supplier selection in theconstruction materials industry and residential housing evaluation. Results indicate thatthe Modified VIKOR increases the number of alternatives included in the resulting setof best alternatives, especially at higher v values, and that normalization techniquessignificantly influence the composition of the compromise set. The findings alsohighlight the sensitivity of results to data structure. Overall, the proposed frameworkoffers a flexible and effective decision-support tool for strategic evaluations underuncertainty.