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Models for restoration decision making for a supply chain network after a cyber attack

This work considers modeling approaches to the problem of restoration decision making by supply chain managers. Specifically, we consider an oil supply chain network that has suffered a cyber attack. As a result of the attack, critical services to the supply chain are lost, and the manager has uncertain information regarding when these services will be available. We first consider a deterministic model where the supply chain manager has complete information about the restoration of damaged services. We extend this model to a stochastic programming model involving several different recovery scenarios. We present computational results for a realistic case study developed with an extensive literature survey. We compare results of the stochastic model with average deterministic results, and show that the average deterministic results do not produce good solutions for an uncertain setting. We conclude with general remarks on how supply chain managers should consider restoration decisions under uncertainty.