Simulation-driven optimization for resilient and sustainable supply chain operations
Year: 2026
Type: article
Abstract: Supply chains (SC) operate in an interconnected and volatile environment in which shocks such as cyberattacks, natural disasters, and geopolitical disruptions can rapidly propagate across upstream and downstream tiers. To support resilient and sustainable operations under these conditions, organizations require decision support tools capable of evaluating disruption impacts and identifying effective recovery strategies. This study proposes a simulation-driven optimization framework that integrates a system dynamics (SD) model on a granular level with a genetic algorithm (GA) to enhance supply chain resilience (SCRE) under multiple disruption scenarios. The SD model parameterizes the dynamic relationships between upstream material availability and downstream productivity, which is calibrated using real operational data. Disruption propagation mechanisms are analyzed by simulating hypothetical disruption scenarios. Productivity losses and recovery trajectories are quantified and compressed into four resilience dimensions: robustness, redundancy, resourcefulness, and rapidity. The GA captures trade-offs among buffering, flexibility, and recovery speed by optimizing the SC parameters against the disruptions at the suppliers through the resilience score. An industrial case study demonstrates the applicability of the approach and illustrates how simulation-driven optimization yields actionable insights for designing resilient SC configurations through ex-ante virtual risk assessment and model-based planning in a risk-free environment.
Language: English
Subfield: Strategy and Management
Domain: Social Sciences
Open Access status: hybrid
Estimated APC paid: $3,790
OpenAlex ID: W7160141433