Scenario-robust pre-disaster planning for multiple relief items

The increasing vulnerability of the population from frequent disasters requires quick and effective responses to provide the required relief through effective humanitarian supply chain distribution networks. We develop scenario-robust optimization models for stocking multiple disaster relief items at strategic facility locations for disaster response. Our models improve the robustness of solutions by easing the difficult, and usually impossible, task of providing exact probability distributions for uncertain parameters in a stochastic programming model. Our models allow decision makers to specify uncertainty parameters (i.e., point and probability estimates) based on their degrees of knowledge, using distribution-free uncertainty sets in the form of ranges. The applicability of our generalized approach is illustrated via a case study of hurricane preparedness in the Southeastern United States. In addition, we conduct simulation studies to show the effectiveness of our approach when conditions deviate from the model assumptions.

Automated summary

This article presents an optimization model for stocking disaster relief items at strategic locations to enhance the effectiveness of humanitarian supply chain distribution networks in responding to disasters. The model provides robust solutions by addressing uncertain parameters using distribution-free uncertainty ranges, which are illustrated through a case study of hurricane preparedness in the Southeastern United States. Simulation studies further demonstrate the effectiveness of the approach in situations where conditions deviate from model assumptions.