Optimizing Protection Strategies for Supply Chains: Comparing Classic Decision-Making Criteria in an Uncertain Environment

The inherent and growing complexity characterizing today's infrastructure systems has considerably increased their vulnerability to external disruptions. Recent world events have demonstrated how damage to one or more infrastructure components can result in disastrous political, social, and economic effects. This, in turn, has fostered the development of sophisticated quantitative methods that identify cost-effective ways of strengthening supply systems in the face of disruption. Stochastic and robust optimization can be used to tackle these strategic problems when uncertainty is present. The uncertainty dealt with in this article is related to the extent to which supply systems can be disrupted. More specifically, we propose and analyze different protection optimization models for minimizing the damage to a system resulting from the disruption of an uncertain number of system components. We compare a cost-based model and two original regret models that, to the best of our knowledge, represent the state of the art in the field of protection in location analysis. Also, we discuss how to build an operational envelope for the models considered, which can be used to identify the range of possible impacts associated with different protection strategies. The models are tested on a benchmark data set and on a new data set that was built using the Census 2001 data of the United Kingdom. We analyze and compare the protection plans generated by the models and provide some useful insights related to the robustness of the different modeling approaches.