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Optimal Dispatch in Emergency Service System via Reinforcement Learning

In: AI and Analytics for Public Health

Author

Listed:
  • Cheng Hua

    (Shanghai Jiaotong University)

  • Tauhid Zaman

    (Yale University)

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.

Suggested Citation

  • Cheng Hua & Tauhid Zaman, 2022. "Optimal Dispatch in Emergency Service System via Reinforcement Learning," Springer Proceedings in Business and Economics, in: Hui Yang & Robin Qiu & Weiwei Chen (ed.), AI and Analytics for Public Health, pages 75-87, Springer.
    • Handle: RePEc:spr:prbchp:978-3-030-75166-1_3
    DOI: 10.1007/978-3-030-75166-1_3
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