  Recent events such as the Heparin tragedy, in which patients lost their lives
due to tainted pharmaceuticals, highlight the necessity for supply chain
designers and planners to consider the risk of even low probability disruptions
in supply chains. One of the most effective ways to hedge against supply chain
network disruptions is to robustly design the supply chain network. This
involves both strategic decisions (e.g., which suppliers to source from, plant
locations, etc.) and tactical decisions (e.g., capacity allocation, etc.).
Since disruptions are modeled as events which occur randomly and may have a
random length, we consider a mixed-integer stochastic model. However, such
network design problems belong to the class of NP-hard problems. Accordingly,
we develop efficient heuristic algorithms and a metaheuristic approach to
obtain acceptable solutions to these types of problems in reasonable runtimes
so that the process of decision making becomes facilitated with no drastic
sacrifices in solution quality.
