Proactive Resource Request for Disaster Response: A Deep Learning-Based Optimization Model

• 成果类型: Article
• 署名作者: Zhang, Hongzhe; Zhao, Xiaohang; Fang, Xiao; Chen, Bintong
• 署名单位: The Chinese University of Hong Kong, Shenzhen; The Chinese University of Hong Kong, Shenzhen; Shanghai University of Finance & Economics; University of Delaware
• 刊物名称: INFORMATION SYSTEMS RESEARCH
• ISSN/ISSBN: 1047-7047
• DOI: 10.1287/isre.2022.0125
• 发表日期: 2024
• 关键词: Inventory Control; POLICY; relief; management; allocation; FRAMEWORK; systems; average; IMPACT

摘要

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., the Federal Emergency Management Agency in the United States). 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 multistakeholder and multiobjective setting through simulations.