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<p>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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月25日

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  • Xin Zhang,
  • Shengjie Wang,
  • Qilong Zhou

摘要

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.