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Health Care Management Science

, Volume 20, Issue 4, pp 517–531 | Cite as

Dynamic ambulance dispatching: is the closest-idle policy always optimal?

  • C. J. JagtenbergEmail author
  • S. Bhulai
  • R. D. van der Mei
Article

Abstract

We address the problem of ambulance dispatching, in which we must decide 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, we 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 we 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, we propose a heuristic for ambulance dispatching that can handle regions with large numbers of ambulances. Our main focus is on minimizing the fraction of arrivals later than a certain threshold time, but we show that with a small adaptation our MDP can also be used to minimize the average response time. We evaluate our policies by simulating a large emergency medical services region in the Netherlands. For this region, we 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, we do not claim that our heuristic is practically preferable over the closest-idle method. However, our result sheds new light on the popular belief that the closest idle dispatch policy is near-optimal when minimizing the fraction of late arrivals.

Keywords

OR in health services Ambulances Emergency medical services Dispatching Markov decision processes 

Notes

Acknowledgments

The authors of this paper would like to thank the Dutch Public Ministry of Health (RIVM) for giving access to the travel times for EMS vehicles in the Netherlands. This research was financed in part by Technology Foundation STW under contract 11986, which we gratefully acknowledge.

References

  1. 1.
    Alanis R, Ingolfsson A, Kolfal B (2013) A Markov chain model for an EMS system with repositioning. Prod Oper Manag 22(1):216–231CrossRefGoogle Scholar
  2. 2.
    Bandara D, Mayorga ME, McLay LA (2012) Optimal dispatching strategies for emergency vehicles to increase patient survivability. Int J Oper Res 15(2):195–214, 8CrossRefGoogle Scholar
  3. 3.
    Bellman R (1957) Dynamic programming princeton university pressGoogle Scholar
  4. 4.
    Bellman R (1957) A Markovian decision process. J Math Mech 6(4):679–684Google Scholar
  5. 5.
    Bjarnason R, Tadepalli P, Fern A (2009) Simulation-based optimization of resource placement and emergency response. In: Proceedings of the 21st Innovative Applications of Artificial Intelligence ConferenceGoogle Scholar
  6. 6.
    Carter G, Chaiken J, Ignall E (1972) Response areas for two emergency units. Oper Res 20(3):571–594CrossRefGoogle Scholar
  7. 7.
    Church RL, Revelle CS (1974) The maximal covering location problem. Pap Reg Sci Assoc 32:101–118CrossRefGoogle Scholar
  8. 8.
    Personal communication with ambulance provider Utrecht (RAVU)Google Scholar
  9. 9.
    Daskin MS (1983) A maximum expected location model: Formulation, properties and heuristic solution. Transp Sci 7:48–70CrossRefGoogle Scholar
  10. 10.
    Dean SF (2008) Why the closest ambulance cannot be dispatched in an urban emergency medical services system. Prehosp Disaster Med 23(02):161–165CrossRefGoogle Scholar
  11. 11.
    Goldberg J, Dietrich R, Chen JM, Mitwasi MG (1990) Validating and applying a model for locating emergency medical services in Tucson, AZ. Euro 34:308–324Google Scholar
  12. 12.
    Jagtenberg CJ, Bhulai S, van der Mei RD (2015) An efficient heuristic for real-time ambulance redeployment. Oper Res Health Care 4:27–35CrossRefGoogle Scholar
  13. 13.
    Jarvis JP (1981) Optimal assignments in a markovian queueing system. Comput Oper Res 8(1):17–23CrossRefGoogle Scholar
  14. 14.
    Kerkkamp RBO (2014) Facility location models in emergency medical service: Robustness and approximations. Master’s thesis, Delft University of TechnologyGoogle Scholar
  15. 15.
    Kuhn M, Johnson K (2013) Applied predictive modeling springer new yorkGoogle Scholar
  16. 16.
    Maxwell MS, Restrepo M, Henderson SG, Topaloglu H (2010) Approximate dynamic programming for ambulance redeployment. INFORMS J Comput 22:226–281CrossRefGoogle Scholar
  17. 17.
    McLay LA, Mayorga ME (2013) A dispatching model for server-to-customer systems that balances efficiency and equity. Manuf Serv Oper Manag 15(2):205–220, 3CrossRefGoogle Scholar
  18. 18.
    McLay LA, Mayorga ME (2013) A model for optimally dispatching ambulances to emergency calls with classification errors in patient priorities. IIE Trans (Inst Ind Eng) 45(1):1–24, 1Google Scholar
  19. 19.
    (2014). Ambulancezorg Nederland. Ambulances in-zicht 2014Google Scholar
  20. 20.
    Puterman M (1994) Markov Decision Processes: Discrete Stochastic Dynamic Programming. Wiley, New YorkCrossRefGoogle Scholar
  21. 21.
    Swoveland C, Uyeno D, Vertinsky I, Vickson R (1973) Ambulance location: a probabilistic enumeration approach. Manag Sci 20(4):686–698CrossRefGoogle Scholar
  22. 22.
    van den Berg PL, van Essen JT, Harderwijk EJ (2014) Comparison of static ambulance location models under reviewGoogle Scholar
  23. 23.
    Yue Y, Marla L, Krishnan R (2012) An efficient simulation-based approach to ambulance fleet allocation and dynamic redeployment. In: AAAI Conference on artificial intelligence (AAAI)Google Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

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

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