Comparative Evaluation of Ratio-Based MCDM Methods: Performance and Ranking Stability under DAM Weighting

Abstract

Ratio-based Multi-Criteria Decision-Making (MCDM) methods provide practical approaches for ranking alternatives, but differences in normalization and aggregation mechanisms can produce different rankings and levels of stability. This study comparatively evaluates ratio-based MCDM methods under a common Data Assessment Method (DAM) weighting framework, focusing on MOORA and ARAS for logistics hub location selection. The study uses a dataset comprising 12 candidate logistics hub locations and six criteria. DAM is first applied to derive criterion weights, after which MOORA and ARAS are implemented using the same decision matrix and weighting configuration. Ranking performance is examined through baseline ranking comparison, while robustness is assessed using systematic criterion-weight perturbations of ±0.05 and ±0.10. Kendall’s Tau is employed to measure ranking consistency, and Mean Absolute Rank Change (MARC) is used to evaluate ranking stability. The DAM results show a relatively balanced criterion-weight distribution, with CLH02 receiving the highest weight of 0.1932. Both methods identify LH6 as the best alternative and LH9 as the least preferred, although several intermediate alternatives exchange positions. MOORA achieves a Kendall’s Tau of 0.9163 and a MARC of 0.3715, whereas ARAS obtains 0.9674 and 0.1840, respectively. These results demonstrate that ARAS provides higher ranking consistency and substantially greater stability under weight perturbations. Therefore, ARAS is identified as the better-performing ratio-based MCDM method for the evaluated logistics hub location problem. The findings highlight the importance of assessing both ranking consistency and stability when selecting MCDM methods for robust decision support.