Journal of Medical Systems

, Volume 34, Issue 1, pp 43–49 | Cite as

An Optimization Model for Locating and Sizing Emergency Medical Service Stations

  • Nusin Coskun
  • Rizvan Erol
Original Paper


Emergency medical services (EMS) play a crucial role in the overall quality and performance of health services. The performance of these systems heavily depends on operational success of emergency services in which emergency vehicles, medical personnel and supporting equipment and facilities are the main resources. Optimally locating and sizing of such services is an important task to enhance the responsiveness and the utilization of limited resources. In this study, an integer optimization model is presented to decide locations and types of service stations, regions covered by these stations under service constraints in order to minimize the total cost of the overall system. The model can produce optimal solutions within a reasonable time for large cities having up to 130 districts or regions. This model is tested for the EMS system of Adana metropolitan area in Turkey. Case study and computational findings of the model are discussed in detail in the paper.


Emergency medical services Location problems Resource allocation Optimization 


  1. 1.
    Dick, W. F., Anglo-American vs. Franco-German emergency medical services system. Prehosp. Disaster Med. 18–1:29–35, 2003.Google Scholar
  2. 2.
    Toronto Emergency Medical Services (
  3. 3.
    Ten Duis, J. H., and van der Werken, C., Trauma care systems in The Netherlands. Int. J. Care Injured. 34:722–727, 2003.Google Scholar
  4. 4.
    Poulymenopoulou, M., Malamateniou, F., and Vassilacopoulos, G., Specifying workflow process requirements for an emergency medical service. J. Med. Syst. 27:4325–335, 2003.CrossRefGoogle Scholar
  5. 5.
    Daskin, M. S., and Dean, L. K., Location of health care facilities. Handbook of OR/MS in Health Care: A Handbook of Methods and Applications. Kluwer:43–76, 2004.Google Scholar
  6. 6.
    Toregas, C., Swain, R., Revelle, C., and Bergman, L., The location of the emergency service facilities. Oper. Res. 19:1363–1373, 1971.zbMATHCrossRefGoogle Scholar
  7. 7.
    Brotcorne, L., Laporte, G., and Semet, F., Invited review: ambulance location and relocation models. Eur. J. Oper. Res. 147–3:451–463, 2003.CrossRefMathSciNetGoogle Scholar
  8. 8.
    Hesse-Owen, S., and Daskin, M., Strategic facility location: a review. Eur. J. Oper. Res. 111–3:423–447, 1998. doi: 10.1016/S0377-2217(98)00186-6.CrossRefGoogle Scholar
  9. 9.
    ReVelle, C., Review, extension and prediction in emergency service siting models. Eur. J. Oper. Res. 40–1:58–69, 1989.CrossRefMathSciNetGoogle Scholar
  10. 10.
    Church, R. L., and ReVelle, C. S., The maximal covering location problem. Pap. Reg. Sci. Assoc. 32:101–118, 1974. doi: 10.1007/BF01942293.CrossRefGoogle Scholar
  11. 11.
    Eaton, D. J., Daskin, M., Simmons, D., Bulloch, B., and Jansma, G., Determining emergency medical service vehicle deployment in Austin, Texas. Interfaces. 15–1:96–108, 1985.CrossRefGoogle Scholar
  12. 12.
    Hogan, K., and Revelle, C., Concepts and applications of backup coverage. Manage. Sci. 32–11:1434–1443, 1986.CrossRefGoogle Scholar
  13. 13.
    Daskin, M. S., A maximal expected covering location model: formulation, properties and heuristic solution. Transp. Sci. 17–1:48–70, 1983.CrossRefGoogle Scholar
  14. 14.
    Fujiwara, O., Makjamroen, T., and Gupta, K. K., Ambulance deployment analysis: a case study of Bangkok. Eur. J. Oper. Res. 31:9–18, 1987. doi: 10.1016/0377-2217(87)90130-5.CrossRefGoogle Scholar
  15. 15.
    Arostegui, M. A., Kadipasaoglu, S. N., and Khumawala, B. M., An empirical comparison of tabu search, simulated annealing, and genetic algorithms for facilities location problems. Int. J. Prod. Econ. 103:742–754, 2006. doi: 10.1016/j.ijpe.2005.08.010.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  1. 1.Cukurova UniversityAdanaTurkey

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