Abstract
This paper presents decentralized capacity planning models for four types of supply chain entities, classified based on functional similarities, to enhance their resilience. Novel resilience metrics are devised to quantify the proximity of backed-up, fortified, and recovered capacities to the amount of disrupted capacity by analyzing post-disruption events (i.e., absorption, adaptation, and restoration). Based on these metrics, bi-objective optimization models are developed to maximize resilience and cost-efficiency simultaneously to select proper business continuity plans contributing to supply chain entities’ proactive and reactive resilience. Finally, a robust-stochastic optimization method is tailored to address the uncertainties associated with the available time to recover and the impact and occurrence of disruptions. Also, disruption scenarios are simulated using a discrete-time Markov chain. Computational tests confirm the proposed models’ robustness, validity, and generality. The results describe essential features of business continuity plans, prescribe optimal decisions to enhance resilience, and predict how sensitive the resiliency and the imposed costs are to disruptions or proactive/reactive planning.
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