A Novel Simplified Yielded Aggregation Index (SYAI) Method for Enhancing Multi-Criteria Decision-Making

Abstract

This research introduces a new MCDM approach known as the Simplified Yielded Aggregation Index (SYAI) to overcome the main problems with traditional frameworks. The first step proposes a new way to normalize goal-type criteria, so decision-makers can assess alternatives according to  their ideal targets, not just the extremes they reach. The second aim suggests using a unified transformation for all criteria, turning every criterion into a single type of value, making the decision clearer and fairer. The third goal involves making a flexible ranking of possibilities by introducing a new weighted closeness score based on parameters around the ideal and anti-ideal references. A variety of scenarios and analyses involving sensitivity were conducted to compare SYAI with recognized MCDM methods, for example, TOPSIS, VIKOR, WASPAS, and SAW. The results displayed that SYAI remained consistent with previous methods, as proved by the high Spearman correlation found in rankings. Having a unified approach and a tunable β parameter allowed the algorithm to respond flexibly to decisions without slowing down, and this is useful in situations like supply chain management and healthcare. The new developments make SYAI well-suited for various real-world uses, as it is both powerful, flexible and scalable.