Resource distribution under spatiotemporal uncertainty of disease spread: Stochastic versus robust approaches - Basciftci, Beste; Yu, Xian; Shen, Siqian (Computers & operations research, 2023)
We consider the problem of optimizing locations of distribution centers (DCs) and plans for distributing resources such as test kits and vaccines, under spatiotemporal uncertainties of disease spread and demand for the resources. We aim to balance the operational cost (including costs of deploying facilities, shipping, and storage) and quality of service (reflected by demand coverage), while ensuring equity and fairness of resource distribution across multiple populations. We compare a sample-based stochastic programming (SP) approach with a distributionally robust optimization (DRO) approach using a moment-based ambiguity set. Numerical studies are conducted on instances of distributing COVID-19 vaccines in the United States and test kits, to compare SP and DRO models with a deterministic formulation using estimated demand and with the current resource distribution plans implemented in the US. We demonstrate the results over distinct phases of the pandemic to estimate the cost and speed of resource distribution depending on scale and coverage, and show the “demand-driven” properties of the SP and DRO solutions. Our results further indicate that if the worst-case unmet demand is prioritized, then the DRO approach is preferred despite of its higher overall cost. Nevertheless, the SP approach can provide an intermediate plan under budgetary restrictions without significant compromises in demand coverage. •We present a generic framework to model resource distribution for epidemic response under spatiotemporal demand uncertainties.•We propose stochastic programming and distributionally robust optimization approaches to address this problem.•We present model extensions for more complex operational settings and derive single-level reformulation of the distributionally robust formulation.•We present two extensive case studies for COVID-19 vaccine distribution in the US and test kit distribution in the State of Michigan over different phases of the pandemic.•The distributionally robust approach provides a better most-restrictive performance in terms of unvaccinated or untested people who qualify, with an overall cost higher than the one of the stochastic programming approach.