Simulation-Aided Analysis of a Deviation-Based Pairwise Assessment Ratio Technique (DEPART) for MCDM

Multi-criteria decision-making (MCDM) methods provide structured approaches for evaluating alternatives based on multiple conflicting criteria. However, many existing MCDM techniques assess alternatives independently, either by aggregating weighted performance scores or by measuring deviations from reference points without a comparative basis. These approaches often fail to account for the relative nature of evaluations within the decision matrix, potentially leading to less reliable rankings. This research introduces a novel Deviation-Based Pairwise Assessment Ratio Technique (DEPART) for MCDM. Unlike conventional methods, DEPART evaluates alternatives through pairwise deviation ratios, ensuring a relative and holistic assessment. By incorporating all available decision data, this approach enhances the reliability and interpretability of rankings. To validate its effectiveness, a simulation-aided analysis is conducted. The evaluation process includes a benchmark example, a simulation analysis with varying criteria weights, and tests on randomly generated datasets with different problem sizes and performance ranges. Correlation coefficients are computed to measure consistency, while robustness is assessed using Analysis of Means (ANOM) and confidence intervals. The results demonstrate that DEPART provides stable and reliable rankings, making it a promising approach for solving MCDM problems.