Health Care Management Science

, Volume 20, Issue 2, pp 165–186 | Cite as

A dynamic ambulance management model for rural areas

Computing redeployment actions for relevant performance measures
  • T. C. van Barneveld
  • S. Bhulai
  • R. D. van der Mei
Article

Abstract

We study the Dynamic Ambulance Management (DAM) problem in which one tries to retain the ability to respond to possible future requests quickly when ambulances become busy. To this end, we need models for relocation actions for idle ambulances that incorporate different performance measures related to response times. We focus on rural regions with a limited number of ambulances. We model the region of interest as an equidistant graph and we take into account the current status of both the system and the ambulances in a state. We do not require ambulances to return to a base station: they are allowed to idle at any node. This brings forth a high degree of complexity of the state space. Therefore, we present a heuristic approach to compute redeployment actions. We construct several scenarios that may occur one time-step later and combine these scenarios with each feasible action to obtain a classification of actions. We show that on most performance indicators, the heuristic policy significantly outperforms the classical compliance table policy often used in practice.

Keywords

Dynamic ambulance management Dynamic relocation Response times Heuristics 

Notes

Acknowledgments

This research was financed in part by Technology Foundation STW under contract 11986, which we gratefully acknowledge. Moreover, we thank the ambulance service provider of Flevoland for providing data.

Conflict of interests

The authors declare that they have no conflict of interest.

Funding

This study was funded by Technology Foundation STW, under contract number 11986.

References

  1. 1.
    Alanis R (2012) Emergency medical services performance under dynamic ambulance redeployment. PhD thesis. University of AlbertaGoogle Scholar
  2. 2.
    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
  3. 3.
    Batta R, Dolan J, Krishnamurthy N (1989) The maximal expected covering location model revisited. Transportation Sci 23:277–287CrossRefGoogle Scholar
  4. 4.
    Brotcorne L, Laporte G, Semet F (2003) Ambulance location and relocation models. Eur J Oper Res 147:451–463CrossRefGoogle Scholar
  5. 5.
    Burkhard RE, Dell’Amico M, Martello S (2009) Assignment Problems, chapter 6. SIAM, PhiladelphiaGoogle Scholar
  6. 6.
    Church R, ReVelle C (1974) The maximal covering location problem. Papers Regional Science Association 32(1):101–118CrossRefGoogle Scholar
  7. 7.
    Daskin M (1983) The maximal expected covering location model: Formulation, properties, and heuristic solution. Transportation Sci 17:48–70CrossRefGoogle Scholar
  8. 8.
    Erkut E, Ingolfsson A, Erdogan G. (2008) Ambulance location for maximum survival. Nav Res Logist 55(1):42–58CrossRefGoogle Scholar
  9. 9.
    Gendreau M, Laporte G, Semet S (2001) A dynamic model and parallel tabu search heuristic for real time ambulance relocation. Parallel Comput 27:1641–1653CrossRefGoogle Scholar
  10. 10.
    Gendreau M, Laporte G, Semet S (2006) The maximal expected coverage relocation problem for emergency vehicles. J Oper Res Soc 57:22–28CrossRefGoogle Scholar
  11. 11.
    Kolesar P, Walker WE (1974) An algorithm for the dynamic relocation of fire companies. Oper Res 22(2):249–274CrossRefGoogle Scholar
  12. 12.
    Larson R (1974) A hypercube queuing model for facility location and redistricting in urban emergency services. Comput Oper Res 1:67–95CrossRefGoogle Scholar
  13. 13.
    Maxwell MS, Restrepo M, Henderson SG, Topaloglu H (2010) Approximate dynamic programming for ambulance redeployment. INFORMS J Comput 22(2):266–281CrossRefGoogle Scholar
  14. 14.
    Naoum-Sawaya J, Elhedhli S (2013) A stochastic optimization model for real-time ambulance redeployment. Comput Oper Res 40:1972–1978CrossRefGoogle Scholar
  15. 15.
    Puterman M (1994) Markov decision processes: Discrete stochastic dynamic programming, 1st edn. John Wiley & Sons, Inc., New YorkCrossRefGoogle Scholar
  16. 16.
    ReVelle CS, Swain RW (1970) Central facilities location. Geogr Anal 2(1):30–42CrossRefGoogle Scholar
  17. 17.
    Rinne H (2008) The Weibull Distribution: A Handbook. Taylor & FrancisGoogle Scholar
  18. 18.
    Schmid V (2012) Solving the dynamic ambulance relocation and dispatching problem using approximate dynamic programming. Eur J Oper Res 219:611–621CrossRefGoogle Scholar
  19. 19.
    Schrijver A (2003) Combinatorial optimization - polyhedra and efficiency, volume A, chapter 17. Springer, Berlin HeidelbergGoogle Scholar
  20. 20.
    Toregas C, Swain R, ReVelle C, Bergman L (1971) The location of emergency facilities. Oper Res 19(6):1363–1373CrossRefGoogle Scholar
  21. 21.
    Wang YH (1993) On the number of successes in independent trials. Stat Sin 3(2):295–312Google Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • T. C. van Barneveld
    • 1
  • S. Bhulai
    • 2
  • R. D. van der Mei
    • 1
  1. 1.Centrum Wiskunde & InformaticaAmsterdamThe Netherlands
  2. 2.VU University AmsterdamAmsterdamThe Netherlands

Personalised recommendations