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Although supply chain disruptions have been under study since the 1990's, they attracted more attention last year due to the COVID-19 pandemic. Not only researchers, but all people realized how disruptions can negatively affect the performance of business, and supply chains. Mitigation strategies are the way to "be better prepared" for such disruptions. This paper aims at evaluating the performance of five different mitigation strategies for a multi-period supply chain under operational disruptions. The disruptions under consideration are capacity, and demand disruptions, whether separate or simultaneous. Integer linear programming is used to design the supply chain network. Monte Carlo simulation is used to evaluate the performance of the proposed mitigation strategies under different disruption scenarios. Results reveal that the mitigation strategies that perform better regarding financial performance measures, perform worse regarding customer satisfaction performance measures. The model helps decision makers to decide the most suitable mitigation strategy according to their priorities.

This paper studies the rescheduling problem of a single machine facing unexpected disruptions in order to determine which parameters can help reducing the negative impacts of these disruptions on schedule performance. A Genetic Algorithm (GA) is used to generate the initial schedule and the updated ones according to a reactive strategy. The performance of event-driven rescheduling and periodic rescheduling policies are compared in terms of total tardiness and total cost of rescheduling. Other factors that may affect rescheduling such as disruption time, disruption duration and number of disruptions are investigated. The sensitivity of results to both due date tightness and cost factor variation is tested. The results showed that the timing of the occurrence of disruption as related to scheduling horizon has a major effect on determining the best rescheduling policy. Event-driven policy is superior to other policies for short infrequent disruptions. It was found that the periodic policy is more appropriate for long and frequent disruptions.