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

Hongzhe Zhang (), Xiaohang Zhao (), Xiao Fang () and Bintong Chen ()
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Hongzhe Zhang: School of Management and Economics and Shenzhen Finance Institute, The Chinese University of Hong Kong, Shenzhen 518172, China
Xiaohang Zhao: School of Information Management and Engineering, Shanghai University of Finance and Economics, Shanghai 200433, China
Xiao Fang: Lerner College of Business and Economics, University of Delaware, Newark 19716, Delaware
Bintong Chen: Lerner College of Business and Economics, University of Delaware, Newark 19716, Delaware

Information Systems Research, 2024, vol. 35, issue 2, 528-550

Abstract: 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.

Keywords: disaster response; disaster management; proactive resource request; deep learning; temporal point process; stochastic optimization (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (2)

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