Supplementary Sequential 3WD-EDAS-E.pdf

Last-mile delivery (LMD) has become a critical focus in supply chain management, primarily due to multiple factors influencing its operational efficiency. Evaluating its performance presents a multi-criteria decision-making challenge, requiring systematic approaches to ensure informed decision-making. This study contributes to the evolving literature on LMD evaluation by introducing methodological improvements within the EDAS (Evaluation based on Distance from the Average Solution) method to generate more intuitive insights into decision-making. First, instead of the L1-metric used in the canonical EDAS, we introduced Euclidean-based EDAS (EDAS-E) to better capture the underlying geometry of the space generated by a relatively high number of decision criteria in LMD assessments. Second, adopting the notion of "thinking in threes," we developed an algorithm to implement a sequential three-way decision (3WD) that enhances explainability and addresses ambiguity in the evaluation process. A study on LMD, lifted from the literature, is used to demonstrate the efficacy of the proposed sequential 3WD-EDAS-E, guiding decision-makers in systematically distinguishing between acceptable, unacceptable, and indeterminate alternatives. The comparative analysis shows high agreement between the results generated by the proposed approach and those obtained from other multi-criteria methods, reinforcing its robustness and applicability.