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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
This holds for the realistic region that we implemented, but does not necessarily hold in general.
- 2.
The fraction of late arrivals.
References
R. Alanis, A. Ingolfsson, B. Kolfal, A Markov chain model for an EMS system with repositioning. Prod. Oper. Manag. 22 (1), 216–231 (2013)
R. Bellman, Dynamic Programming (Princeton University Press, Princeton, 1957)
R. Bellman, A Markovian decision process. J. Math. Mech. 6 (4), 679–684 (1957)
R. Bjarnason, P. Tadepalli, A. Fern, Simulation-based optimization of resource placement and emergency response, in Proceedings of the Twenty-First Innovative Applications of Artificial Intelligence Conference, 2009
G. Carter, J. Chaiken, E. Ignall, Response areas for two emergency units. Oper. Res. 20 (3), 571–594 (1972)
R.L. Church, C.S. Revelle, The maximal covering location problem. Pap. Reg. Sci. Assoc. 32, 101–118 (1974)
M.S. Daskin, A maximum expected location model: formulation, properties and heuristic solution. Transp. Sci. 7, 48–70 (1983)
S.F. Dean, Why the closest ambulance cannot be dispatched in an urban emergency medical services system. Prehosp. Disaster Med. 23 (02), 161–165 (2008)
J. Goldberg, R. Dietrich, J.M. Chen, M.G. Mitwasi, Validating and applying a model for locating emergency medical services in Tucson, AZ. Euro 34, 308–324 (1990)
C.J. Jagtenberg, S. Bhulai, R.D. van der Mei, An efficient heuristic for real-time ambulance redeployment. Oper. Res. Health Care 4, 27–35 (2015)
R.B.O. Kerkkamp, Optimising the deployment of emergency medical services. Master’s thesis, Delft University of Technology, 2014
M.S. Maxwell, M. Restrepo, S.G. Henderson, H. Topaloglu, Approximate dynamic programming for ambulance redeployment. INFORMS J. Comput. 22, 226–281 (2010)
M. Puterman, Markov Decision Processes: Discrete Stochastic Dynamic Programming (Wiley, New York, 1994)
V. Schmid, Solving the dynamic ambulance relocation and dispatching problem using approximate dynamic programming. Eur. J. Oper. Res. 219 (3), 611–621 (2012)
C. Swoveland, D. Uyeno, I. Vertinsky, R. Vickson, Ambulance location: a probabilistic enumeration approach. Manag. Sci. 20 (4), 686–698 (1973)
P.L. van den Berg, J.T. van Essen, E.J. Harderwijk, Comparison of static ambulance location models. Under review, 2014
Y. Yue, L. Marla, R. Krishnan, An efficient simulation-based approach to ambulance fleet allocation and dynamic redeployment, in AAAI Conference on Artificial Intelligence (AAAI), July 2012
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Appendix: Notation
Appendix: Notation
Notation in this chapter | Common notation |
---|---|
\(\mathcal{A}_{s}\) | A(s) |
R(s, a) | r a(s) |
p a(s, s′) | p(s | s′, a) |
V i (s) | V t (s) |
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this chapter
Cite this chapter
Jagtenberg, C.J., Bhulai, S., van der Mei, R.D. (2017). Optimal Ambulance Dispatching. In: Boucherie, R., van Dijk, N. (eds) Markov Decision Processes in Practice. International Series in Operations Research & Management Science, vol 248. Springer, Cham. https://doi.org/10.1007/978-3-319-47766-4_9
Download citation
DOI: https://doi.org/10.1007/978-3-319-47766-4_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-47764-0
Online ISBN: 978-3-319-47766-4
eBook Packages: Business and ManagementBusiness and Management (R0)