Resource distribution under spatiotemporal uncertainty of disease spread: Stochastic versus robust approaches
Resource distribution under spatiotemporal uncertainty of disease spread: Stochastic versus robust approaches
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DOI：
10.1016/j.cor.2022.106028
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发表时间：
2021-03
期刊：
影响因子：
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通讯作者：
Beste Basciftci;Xian Yu;Siqian Shen
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文献类型：
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作者：
Beste Basciftci;Xian Yu;Siqian Shen
我们考虑了在疾病传播和资源需求的时空不确定性下，配送中心(DC)的最优位置和分配资源(如检测试剂盒和疫苗)的计划。我们的目标是平衡运营成本(包括部署设施、运输和存储的成本)和服务质量(通过需求覆盖来反映)，同时确保在多个人口之间公平和公平地分配资源。我们比较了基于样本的随机规划(SP)方法和使用基于矩的模糊集的分布稳健优化(DRO)方法。对在美国分发新冠肺炎疫苗和试剂盒的实例进行了数值研究，以比较SP和DRO模型与使用估计需求的确定性公式以及美国实施的当前资源分配计划的差异。我们展示了大流行不同阶段的结果，以估计取决于规模和覆盖范围的资源分配的成本和速度，并展示了SP和DRO解决方案的“需求驱动”属性。我们的结果进一步表明，如果最坏情况下未满足的需求是优先的，那么DRO方法是首选的，尽管它的总成本更高。尽管如此，SP方法可以在预算限制下提供一个中间计划，而不会在需求覆盖方面做出重大妥协。
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.