<![CDATA[Multi-Criteria Decision Making (MCDM) methods are widely applied in Decision Support Systems (DSS) to address selection problems involving multiple criteria. However, differences in aggregation mechanisms may affect ranking consistency, stability, and robustness under changing decision conditions. This study presents a benchmarking analysis of five aggregation-based MCDM methods, namely Simple Additive Weighting (SAW), Weighted Aggregated Sum Product Assessment (WASPAS), Additive Ratio Assessment (ARAS), Complex Proportional Assessment (COPRAS), and Multi-Attributive Ideal-Real Comparative Analysis (MAIRCA), for employee selection. Ranking consistency is evaluated using Spearman rank correlation, while stability and robustness are assessed using the Ranking Stability Index and Ranking Retention Rate. SAW, WASPAS, ARAS, and COPRAS achieve perfect consistency with the reference ranking, with a Spearman coefficient of 1.0000, while MAIRCA obtains 0.9879. ARAS and COPRAS achieve the highest stability, with an RSI of 97.22%, and robustness, with an RRR of 86.11%. SAW, WASPAS, and MAIRCA achieve an RSI of 95.56% and an RRR of 77.78%. Ranking changes are mainly limited to an exchange between Candidates 1 and 9, while Candidate 3 remains first across all scenarios. These findings suggest that ARAS and COPRAS exhibit comparatively higher ranking stability under the experimental conditions considered in this study.]]>
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