# Optimal Dispatch in Emergency Service System via Reinforcement Learning

arXiv: https://arxiv.org/abs/2010.07513 · HTML: https://mlsanity.com/paper/2010.07513/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Oct 2020.

## Built on by (most cited first, 1 of 1)

- [Optimization-Augmented Machine Learning for Vehicle Operations in Emergency Medical Services](https://mlsanity.com/paper/2503.11848/) (2025)

## Abstract (arXiv)

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.