Soft cluster-rectangle method for eliciting criteria weights in multi-criteria decision-making

Subjective weighting methods are widely employed to determine criteria weights in multi-criteria decision-making (MCDM) environment. Inputs from decision-makers, including opinions, assessments, assumptions, evaluations, interpretations, expectations, and judgments, are primarily relied upon in these methods. Significant challenges are faced due to two primary factors: the inherent uncertainty in inputs and the process of pairwise comparisons. These challenges increase the uncertainty regarding the derived weights, raising concerns about the reliability of such approaches. This paper introduces a novel MCDM method called Soft Clusters-Rectangles (SCR) to overcome such limitations. This method distinguishes itself by avoiding pairwise comparisons and adopting a fuzzy approach to address uncertainty. Weights are calculated based on criteria membership values across three defined clusters, namely immaterial, mediocre, and vital. Each cluster represents a distinct range of importance. Analytic geometry is also used by computing the areas of multiple rectangles to determine the final weights. The application of SCR method is demonstrated through a case study on autonomous vehicle route selection problem. The results are thoroughly examined through comprehensive analysis. The findings reveal that this method not only avoids the challenges posed by pairwise comparison-based methodologies but also exhibits similarities with objective weighting techniques, often yielding results more comparable to those produced by hybrid methods.