  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 for processing and transporting
products to downstream entities. Under disruptive events, lead time and
capacities may vary, which affects the overall SC performance. 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 involving costs related to transportation, shortage, and delivery
lateness. We also develop a discrete-event simulation framework to evaluate the
performance of solutions to the MIP model under lead time uncertainty. Via
extensive numerical studies, we show how the attributes of SC entities affect
the performance, so that we can improve SC design and operations under various
uncertainties.
