Optimal Dispatch in Emergency Service System via Reinforcement Learning
This addresses resource efficiency for emergency service decision-makers, but it is incremental as it builds on existing reinforcement learning methods for a specific domain.
The paper tackled the ambulance dispatch problem by modeling it as an average-cost Markov decision process and proposing a temporal-difference learning approach using post-decision states, which outperformed a benchmark myopic policy in numerical experiments, suggesting performance improvements for emergency response departments at minimal cost.
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.