This study proposes a simulation-optimization framework to support the design of resilient supply chain systems under prolonged disruption risks, such as those experienced during a pandemic. A discrete-event simulation model, integrated with the OptQuest optimization engine in Arena, is developed to evaluate and optimize key decisions in the supply chain, including inventory control and order allocation strategies. The model incorporates multi-sourcing and pre-positioning approaches to improve system robustness. Randomized disruption scenarios affecting suppliers and manufacturers are used to assess supply chain performance in terms of total cost and customer service level. The results provide actionable insights for systems engineers and decision-makers seeking to balance efficiency and resilience in complex supply chain networks under uncertainty.
Phung et al. (Mon,) studied this question.
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