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

TL;DR

This paper introduces a deep learning-based optimization model for disaster resource requests, proactively predicting future demands and optimizing request quantities to improve disaster response efficiency.

Contribution

It develops a novel deep learning method for demand prediction and formulates a stochastic optimization model for proactive resource requesting in disaster response.

Findings

Outperforms existing methods in real-world and simulated data

Effective in multi-stakeholder, multi-objective scenarios

Provides a new framework for proactive disaster resource management

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., 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.