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Ambulance Service Planning: Simulation and Data Visualisation

  • Shane G. Henderson
  • Andrew J. Mason
Part of the International Series in Operations Research & Management Science book series (ISOR, volume 70)

Summary

The ambulance-planning problem includes operational decisions such as choice of dispatching policy, strategic decisions such as where ambulances should be stationed and at what times they should operate, and tactical decisions such as station location selection. Any solution to this problem requires careful balancing of political, economic and medical objectives. Quantitative decision processes are becoming increasingly important in providing public accountability for the resource decisions that have to be made. This chapter discusses a simulation and analysis software tool ‘BartSim’ that was developed as a decision support tool for use within the St. John Ambulance Service (Auckland Region) in New Zealand (St. Johns). The novel features incorporated within this study include
  • the use of a detailed time-varying travel model for modelling travel times in the simulation,

  • methods for reducing the computational overhead associated with computing time-dependent shortest paths in the travel model,

  • the direct reuse of real data as recorded in a database (trace-driven simulation), and

  • the development of a geographic information sub-system (GIS) within BartSim that provides spatial visualisation of both historical data and the results of what-if simulations.

Our experience with St. Johns, and discussions with emergency operators in Australia, North America, and Europe, suggest that emergency services do not have good tools to support their operations management at all levels (operational, strategic and tactical). Our experience has shown that a customized system such as BartSim can successfully combine GIS and simulation approaches to provide a quantitative decision support tool highly valued by management. Further evidence of the value of our system is provided by the recent selection of BartSim by the Metropolitan Ambulance Service for simulation of their operations in Melbourne, Australia. This work has led to the development of BartSim’s successor, Siren (Simulation for Improving Response times in Emergency Networks), which includes many enhancements to handle the greater complexities of the Melbourne operations.

Key words

Ambulance service planning Simulation 

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References

  1. [1]
    Swersey, A.J. (1994). The deployment of police, fire, and emergency medical units. In Pollock, S.M., M.H. Rothkopf, and A. Barnett, eds., Operations Research and the Public Sector. North Holland, Amsterdam.Google Scholar
  2. [2]
    Swoveland, C., D. Uyeno, I. Vertinsky, and R. Vickson (1973). Ambulance location: A probabilistic enumeration approach. Management Science, 20, 686–698.Google Scholar
  3. [3]
    Larson, R.C. and A.R. Odoni (1981). Urban Operations Research. Prentice-Hall, Englewood Cliffs, NJ. Also available at http://web.mit.edu/urban_or_book/www/book/Google Scholar
  4. [4]
    Brandeau, M.L. and R.C, Larson (1986). Extending and applying the hypercube queueing model to deploy ambulances in Boston. In A. Swersey and E. Ignall, eds. Delivery of Urban Services, TIMS Studies in Management Sciences 22, Elsevier. 121–153.Google Scholar
  5. [5]
    Savas, E.S. (1969). Simulation and cost-effectiveness analysis of New York’s emergency ambulance service. Management Science, 15, B608–B627.Google Scholar
  6. [6]
    Fitzsimmons, J.A. (1971). An emergency medical system simulation model. Proceedings of the 1971 Winter Simulation Conference. New York. 18–25.Google Scholar
  7. [7]
    Fitzsimmons, J.A. (1973). A methodology for emergency ambulance deployment. Management Science, 19, 627–636.Google Scholar
  8. [8]
    Fujiwara, O., T. Makjamroen, and K.K. Gupta (1987). Ambulance deployment analysis: A case study of Bangkok. European Journal of Operational Research, 31, 9–18.CrossRefGoogle Scholar
  9. [9]
    Daskin, M.S. (1983). A maximum expected coverage location model: Formulation, properties and heuristic solution. Transportation Science, 17, 48–70.Google Scholar
  10. [10]
    Lubicz, M. and Z. Mielczarek (1987). Simulation modeling of emergency medical services. European Journal of Operational Research, 29, 178–185.CrossRefGoogle Scholar
  11. [11]
    Ingolfsson, A., E. Erkut, and S. Budge (2003). Simulation of single start station for Edmonton EMS. Journal of the Operational Research Society, 54, 736–746.CrossRefGoogle Scholar
  12. [12]
    Erkut, E., R. Fenske, S. Kabanuk, Q. Gardiner, and J. Davis (2001). Improving the emergency service delivery in St. Albert. INFOR, 39, 416–433.Google Scholar
  13. [13]
    Harewood, S.I. (2002). Emergency ambulance deployment in Barbados: A multi-objective approach. Journal of the Operational Research Society, 53, 185–192.zbMATHGoogle Scholar
  14. [14]
    Ingolfsson, A., S. Budge, and E. Erkut (2003). Optimal ambulance location with random delays and travel times. Preprint. University of Alberta School of Business, Edmonton, Alberta, Canada.Google Scholar
  15. [15]
    Brotcorne, L., G. Laporte, and F. Semet (2003). Ambulance location and relocation models. European Journal of Operational Research, 147, 451–463.CrossRefMathSciNetGoogle Scholar
  16. [16]
    Auckland Regional Transport (1994). Auckland Transport Models Project: Technical Working Paper 1 ‘Network Development And Inventory,’ Environment Division, Auckland Regional Council, Auckland, New Zealand.Google Scholar
  17. [17]
    Carson, Y.M. and R. Batta (1990). Locating an ambulance on the Amherst Campus of the State University of New York at Buffalo. Interfaces, 20, 43–49.Google Scholar
  18. [18]
    Kolesar, P. (1975). A model for predicting average fire engine travel times. Operations Research, 23, 603–614.Google Scholar
  19. [19]
    Kolesar, P., W. Walker and H. Hausner (1975). Determining the relation between fire engine travel times and travel distances in New York City. Operations Research, 23, 614–627.Google Scholar
  20. [20]
    Peters, J. and G.B. Hall (1999). Assessment of ambulance response performance using a geographic information system. Social Science and Medicine, 49, 1551–1566.PubMedGoogle Scholar
  21. [21]
    Pidd, M., F.N. de Silva, and R.W. Eglese (1996). A simulation model for emergency evacuation. European Journal of Operational Research, 90, 413–419.CrossRefGoogle Scholar
  22. [22]
    Bratley, P., B.L. Fox, and L.E. Schrage (1987). A Guide to Simulation. Springer, New York.Google Scholar
  23. [23]
    Pritsker, A. (1998). Life and death decisions. OR/MS Today, August.Google Scholar
  24. [24]
    Law, A.M. and W.D. Kelton. (2000). Simulation Modeling and Analysis, 3rd ed. McGraw-Hill, Boston, MA.Google Scholar
  25. [25]
    Papadimitriou, C.H. and K. Steiglitz (1982). Combinatorial Optimization: Algorithms and Complexity. Prentice Hall, Englewood Cliffs, NJ.Google Scholar

Copyright information

© Springer Science + Business Media, Inc. 2005

Authors and Affiliations

  • Shane G. Henderson
    • 1
  • Andrew J. Mason
    • 2
  1. 1.Department of Operations Research and Industrial EngineeringCornell UniversityIthaca
  2. 2.Department of Engineering ScienceUniversity of AucklandAucklandNew Zealand

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