Optimal Dispatch in Emergency Service System via Reinforcement Learning

Cheng Hua (cheng.hua@sjtu.edu.cn) and Tauhid Zaman (tauhid.zaman@yale.edu)
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Cheng Hua: Shanghai Jiaotong University
Tauhid Zaman: Yale University

A chapter in AI and Analytics for Public Health, 2022, pp 75-87 from Springer

Abstract: Abstract In the United States, medical responses by fire departments over the last four decades increased by 367%. This had made it critical to decision makers in emergency response departments that existing resources are efficiently used. In this paper, we model the ambulance dispatch problem as an average-cost Markov decision process and present a policy iteration approach to find an optimal dispatch policy. We then propose an alternative formulation using post-decision states that is shown to be mathematically equivalent to the original model, but with a much smaller state space. We present a temporal difference learning approach to the dispatch problem based on the post-decision states. In our numerical experiments, we show that our obtained temporal-difference policy outperforms the benchmark myopic policy. Our findings suggest that emergency response departments can improve their performance with minimal to no cost.

Keywords: Reinforcement learning; Markov decision process; Emergency service system; Dispatch (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:spr:prbchp:978-3-030-75166-1_3

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DOI: 10.1007/978-3-030-75166-1_3

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