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Optimal Ambulance Dispatching

  • C. J. JagtenbergEmail author
  • S. Bhulai
  • R. D. van der Mei
Chapter
Part of the International Series in Operations Research & Management Science book series (ISOR, volume 248)

Abstract

This chapter considers the ambulance dispatch problem, in which one must decide which ambulance to send to an incident in real time. In practice as well as in literature, it is commonly believed that the closest idle ambulance is the best choice. This chapter describes alternatives to the classical closest idle ambulance rule. Our first method is based on a Markov decision problem (MDP), which constitutes the first known MDP model for ambulance dispatching. Moreover, in the broader field of dynamic ambulance management, this is the first MDP that captures more than just the number of idle vehicles, while remaining computationally tractable for reasonably-sized ambulance fleets. We analyze the policy obtained from this MDP, and transform it to a heuristic for ambulance dispatching that can handle the real-time situation more accurately than our MDP states can describe. We evaluate our policies by simulating a realistic emergency medical services region in the Netherlands. For this region, we show that our heuristic reduces the fraction of late arrivals by 13% compared to the “closest idle” benchmark policy. This result sheds new light on the popular belief that deviating from the closest idle dispatch policy cannot greatly improve the objective.

Keywords

Emergency Medical Service Mixed Integer Linear Programming Average Response Time Target Time Late Arrival 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

The authors of this chapter would like to thank the Dutch Public Ministry of Health (RIVM) for giving access to their estimated travel times for EMS vehicles in the Netherlands. This research was financed in part by Technology Foundation STW under contract 11,986, which we gratefully acknowledge.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • C. J. Jagtenberg
    • 1
    Email author
  • S. Bhulai
    • 2
  • R. D. van der Mei
    • 1
  1. 1.StochasticsCWIAmsterdamThe Netherlands
  2. 2.Faculty of SciencesVU University AmsterdamAmsterdamThe Netherlands

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