<![CDATA[Multi-Criteria Decision Making (MCDM) methods are widely used to address complex decision problems involving multiple alternatives and criteria with different characteristics. However, ranking outcomes may be sensitive to normalization mechanisms, criterion weights, and aggregation processes, particularly when benefit and cost criteria are evaluated simultaneously. This study proposes the Enhanced Root Assessment Method (ERAM), an improved root-based MCDM framework designed to provide a more structured and balanced evaluation of alternative performance while improving ranking stability. ERAM integrates modified normalization, weighted normalization, positive and negative aggregation, Relative Benefit–Penalty Balance, and an Enhanced Root Assessment Index to construct the final preference measure. The proposed method is evaluated using a new store location selection dataset comprising eight alternatives and six criteria. Its performance is examined through comparative analysis with SAW, WASPAS, GRA, and MOORA, together with sensitivity analysis using ±0.05 changes in criterion weights. The results show that ERAM maintains a stable ranking structure under weight variations and achieves a strong Spearman correlation of 0.9286 with the reference ranking, exceeding WASPAS (0.9048) and SAW, GRA, and MOORA (0.8810). These findings demonstrate that ERAM can produce consistent rankings while maintaining robustness against moderate changes in criterion importance. Therefore, ERAM provides a structured and robust framework for multi-criteria ranking problems involving heterogeneous criteria and uncertain decision preferences.]]>
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