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

Area of Science:

• Operations Research
• Supply Chain Management
• Decision Science

Background:

• Traditional supply chain risk models often silo risk assessment and mitigation strategy design.
• This separation hinders effective decision-making, especially when facing uncertainty and complex network interdependencies.

Purpose of the Study:

• To develop an integrated decision framework that unifies risk prioritization and mitigation portfolio selection for supply chains.
• To enhance actionable decision-making under uncertainty by linking preference-based evaluations with optimization.

Main Methods:

• Integration of the Best-Worst Method (BWM) for expert-based criterion weighting.
• Application of VIKOR compromise ranking to prioritize disruption exposures.
• Utilization of evolutionary multi-objective optimization for selecting cost-constrained mitigation portfolios.
• Parameterization using data from 100 industry experts, operational indicators, and simulation scenarios.

Main Results:

• The integrated framework achieved a 46% reduction in expected service loss and a 34% reduction in time-to-recovery compared to a baseline without coordinated mitigation.
• Sensitivity analyses confirmed the stability and robustness of the prioritization and portfolio selection outcomes across various parameters and scenarios.

Conclusions:

• Linking preference-based evaluation (BWM, VIKOR) with optimization-based design enables systematic identification of effective supply chain mitigation strategies.
• The proposed unified framework supports more informed and actionable decisions for managing supply chain disruptions under realistic constraints.