Date of Award


Document Type


Degree Name

Doctor of Philosophy (PhD)

Legacy Department

Industrial Engineering


Kurz, Mary E

Committee Member

Mason , Scott J

Committee Member

Mayorga , Maria E

Committee Member

Sharp , Julia L

Committee Member

Taaffe , Kevin M


Recent events, such as the Heparin tragedy, highlight the necessity for designers and planners of supply chain networks to consider the risk of disruptions in spite of their low probability of occurrence. One effective way to hedge against supply chain network disruptions is to have a robustly designed supply chain network. This involves strategic decisions, such as choosing which markets to serve, which suppliers to source from, the location of plants, the types of facilities to use, and tactical decisions, such as production and capacity allocation. In this dissertation, we focus on models for designing supply chain networks that are resilient to disruptions.
We consider two types of decision making policies. A risk-neutral decision making policy is based on the cost minimization approach, and the decision-maker defines the set of decisions that minimize expected cost. We also consider a risk-averse policy wherein rather than selecting facilities that minimize expected cost, the decision-maker uses a Conditional Value-at-Risk approach to measure and quantify risk. However, such network design problems belong to class of NP hard problems. Accordingly, we develop efficient heuristic algorithms and metaheuristic approaches to obtain acceptable solutions to these types of problems in reasonable runtimes so that the decision making process is facilitated with at most a moderate reduction in solution quality. Finally, we perform statistical analyses (e.g., logistic regression) to assess the likelihood of selection for each facility. These models allow us to identify the factors that impact facility selection in both the risk-neutral and risk-averse policies.