Towards Robust Objective Decision-Making: Sensitivity Analysis and Comparative Evaluation of the EORS Approach

The increasing complexity of modern decision-making driven by technological advancement and the multifaceted nature of socio-economic and environmental challenges demands robust, transparent, and interpretable multi-criteria decision-making (MCDM) methods. This study evaluates the recently proposed Exhaustive Objective Ranking Solution (EORS), an objective MCDM technique that explores the entire solution space and utilizes kernel density estimation (KDE) to identify the most representative alternative based on preference value distributions. A real-world case study on the optimal site selection for floating photovoltaic (FPV) systems is employed to validate the method’s effectiveness and compare its performance with established objective weighting approaches (standard deviation, entropy, and CRITIC). The analysis focuses on ranking consistency, stability, and correlation across methods. Additionally, a two-stage sensitivity analysis examines the robustness of EORS to variations in the KDE bandwidth parameter and perturbations in the decision matrix. Results show that EORS provides distinct and stable rankings, with enhanced interpretability through confidence measures, making it suitable for high-stakes decision contexts. Future research directions include extending EORS to uncertain environments and hybridizing it with expert-based weighting to improve adaptability and generalizability.