Optimal Dispatch in Emergency Service System via Reinforcement Learning

• URL: http://arxiv.org/abs/2010.07513v1
• Date: Thu, 15 Oct 2020 04:37:41 GMT
• Title: Optimal Dispatch in Emergency Service System via Reinforcement Learning
• Authors: Cheng Hua and Tauhid Zaman
• Abstract summary: In the United States, medical responses by fire departments over the last four decades increased by 367%. 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. Our findings suggest that emergency response departments can improve their performance with minimal to no cost.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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