Disaster response is critical to save lives and reduce damages in the aftermath of a disaster. Fundamental to disaster response operations is the management of disaster relief resources. To this end, a local agency (e.g., a local emergency resource distribution center) collects demands from local communities affected by a disaster, dispatches available resources to meet the demands, and requests more resources from a central emergency management agency (e.g., Federal Emergency Management Agency in the U.S.). Prior resource management research for disaster response overlooks the problem of deciding optimal quantities of resources requested by a local agency. In response to this research gap, we define a new resource management problem that proactively decides optimal quantities of requested resources by considering both currently unfulfilled demands and future demands. To solve the problem, we take salient characteristics of the problem into consideration and develop a novel deep learning method for future demand prediction. We then formulate the problem as a stochastic optimization model, analyze key properties of the model, and propose an effective solution method to the problem based on the analyzed properties. We demonstrate the superior performance of our method over prevalent existing methods using both real world and simulated data. We also show its superiority over prevalent existing methods in a multi-stakeholder and multi-objective setting through simulations.

翻译：灾害响应对于挽救生命和减少灾后损失至关重要。灾害响应行动的基础是救灾资源的管理。为此，地方机构（例如，地方应急资源分配中心）收集受灾社区的需求，调配可用资源以满足这些需求，并向中央应急管理机构（例如，美国联邦紧急事务管理局）请求更多资源。先前关于灾害响应的资源管理研究忽视了决定地方机构请求资源最优数量的问题。针对这一研究空白，我们定义了一个新的资源管理问题，该问题通过同时考虑当前未满足的需求和未来需求，主动决策请求资源的最优数量。为解决此问题，我们考虑了问题的显著特征，并开发了一种用于未来需求预测的新型深度学习方法。然后，我们将该问题建模为随机优化模型，分析了模型的关键性质，并基于这些性质提出了一种有效的求解方法。我们使用真实数据和模拟数据证明了我们的方法在性能上优于现有主流方法。此外，通过多利益相关方和多目标场景的模拟，我们展示了其相对于现有主流方法的优越性。