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Supply chain (SC) risk management is influenced by both spatial and temporalattributes of different entities (suppliers, retailers, and customers). Eachentity has given capacity and lead time for processing and transportingproducts to downstream entities. Under disruptive events, lead time andcapacities may vary, which affects the overall SC performance. There have beenmany studies on SC disruption mitigation, but often without considering leadtime and the magnitude of lateness. In this paper, we formulate a mixed-integerprogramming (MIP) model to optimize SC operations via a routing and schedulingapproach, to model the delivery time of products at different entities as theyflow throughout the SC network. We minimize a weighted sum of multipleobjectives involving costs related to transportation, shortage, and deliverylateness. We also develop a discrete-event simulation framework to evaluate theperformance of solutions to the MIP model under lead time uncertainty. Viaextensive numerical studies, we show how the attributes of SC entities affectthe performance, so that we can improve SC design and operations under variousuncertainties.

A Multi-objective Mixed-integer Programming Approach for Supply Chain Disruption Response with Lead-Time Awareness