An integrated multi-criteria and evolutionary optimization framework for supply chain disruption risk prioritization and mitigation.

Supply chain disruption risk models often separate risk prioritization from mitigation design, limiting their ability to support actionable decision-making under uncertainty. This study addresses this gap by integrating the Best-Worst Method (BWM), VIKOR compromise ranking, and evolutionary multi-objective optimization into a unified decision framework. BWM is used to derive consistent expert-based criterion weights, VIKOR identifies compromise-prioritized disruption exposures, and evolutionary optimization selects cost-constrained mitigation portfolios. The framework is parameterized using data from 100 industry experts, supported by archival operational indicators and simulation-based disruption scenarios calibrated to reflect multi-tier network behavior. Expert judgments are validated through consistency ratios, content validity measures, and inter-rater agreement. The model is applied to a 15-node supply network and evaluated against a baseline without coordinated mitigation. Results show that the selected portfolio achieves a 46% reduction in expected service loss and a 34% reduction in time-to-recovery relative to baseline conditions. Sensitivity analyses across weight aggregation, compromise parameters, and disruption scenarios confirm the stability of prioritization and portfolio selection outcomes. The findings demonstrate that linking preference-based evaluation with optimization-based design enables systematic identification of mitigation strategies under realistic constraints. All data, parameter settings, and computational procedures are provided in the Supplementary Materials to support full reproducibility.