Scenario-robust pre-disaster planning for multiple relief items

Muer Yang (), Sameer Kumar (), Xinfang Wang () and Michael J. Fry ()
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Muer Yang: University of St. Thomas
Sameer Kumar: University of St. Thomas
Xinfang Wang: Parker College of Business, Georgia Southern University
Michael J. Fry: University of Cincinnati

Annals of Operations Research, 2024, vol. 335, issue 3, No 13, 1266 pages

Abstract: Abstract 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.

Keywords: Humanitarian logistics; Mixed-integer optimization; OR in disaster relief; Stochastic programming; Scenario-robust optimization (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10479-021-04237-3

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