A Lead-Time-Aware Decomposition Approach to Optimize Disruption Response in Supply Chains

Supply chain (SC) risk management is influenced by both spatial and temporal attributes of different entities (suppliers, retailers, and customers). Each entity has given capacity and lead time to process and transport products to downstream entities. In disruptive events, lead times and capacities may vary, which affects the overall performance of SC. There have been many studies on SC disruption mitigation, but often without considering lead time and the magnitude of lateness. In this paper, we formulate a mixed integer programming (MIP) model to optimize SC operations via a routing and scheduling approach, to model the delivery time of products at different entities as they flow throughout the SC network. We minimize a weighted sum of multiple objectives that involve costs related to transportation, shortages, and delivery lateness. We further develop a Benders decomposition algorithm for speeding up the computation of the NP-hard MIP model. We also develop a discrete-event simulation framework to evaluate the performance of solutions to the MIP model under lead time uncertainty. Through extensive numerical studies, we show how the attributes of SC entities affect the performance, so that we can improve the SC design and operations under various uncertainties.