Abstract
The Simple Additive Weighting (SAW) method is a widely recognized and straightforward approach for multi-criteria decision-making. Yet it is prone to ranking instability due to its sensitivity to weight variations, normalization scales, and extreme values. To overcome these challenges, this study introduces a Modified SAW method with Ideal Distance Compensation (SAW-I), which integrates a distance-based adjustment relative to positive and negative ideal solutions within the traditional weighted summation framework. This enhancement considers not only total scores but also the relative position of each alternative in the decision space. The method was applied to two case studies. When compared to other MCDM techniques—SMART, MOORA, GRA, MAUT, WP, and WASPAS-SAW-I demonstrated very high Spearman rank correlations, approaching 1, confirming strong consistency and reliability. Sensitivity analyses further indicated that SAW-I maintains stable rankings even under variations in weights, highlighting its robustness, adaptability, and effectiveness. The sensitivity analysis shows highly stable performance, demonstrating perfect correlation (1.0000) with SMART and WP, and very strong correlation (0.9762) with MOORA, GRA, MAUT, and WASPAS. These results confirm that SAW-I provides high compatibility and consistent rankings, making it more reliable and robust.
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