Dynamic ambulance dispatching

The authors address the problem of ambulance dispatching, in which there must be decided which ambulance to send to an incident in real time. In practice, it is commonly believed that the ‘closest idle ambulance’ rule is near-optimal and it is used throughout most literature. In this paper, the authors present alternatives to the classical closest idle ambulance rule. Most ambulance providers as well as researchers focus on minimizing the fraction of arrivals later than a certain threshold time, and they show that significant improvements can be obtained by our alternative policies. The first alternative is based on a Markov decision problem (MDP), that models more than just the number of idle vehicles, while remaining computationally tractable for reasonably-sized ambulance fleets. Second, they propose a heuristic for ambulance dispatching that can handle regions with large numbers of ambulances. Their main focus is on minimizing the fraction of arrivals later than a certain threshold time, but they show that with a small adaptation our MDP can also be used to minimize the average response time. They evaluate our policies by simulating a large emergency medical services region in the Netherlands. For this region, they show that our heuristic reduces the fraction of late arrivals by 18 % compared to the ‘closest idle’ benchmark policy. A drawback is that this heuristic increases the average response time (for this problem instance with 37 %). Therefore, they do not claim that their heuristic is practically preferable over the closest-idle method. However, their result sheds new light on the popular belief that the closest idle dispatch policy is near-optimal when minimizing the fraction of late arrivals.