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A minimum expected response model: Formulation, heuristic solution, and application

  • Rajagopalan, Hari K.
  • Saydam, Cem
Registered author(s):

    Responding to true emergencies in the shortest possible time saves lives, prevents permanent injuries and reduces suffering. Most covering models consider an emergency cover if an ambulance is available within a given time or distance threshold. From a modeling perspective, shorter or longer responses within this threshold are all tallied as covered; conversely, the emergencies immediately outside the threshold are considered uncovered. However, if the shorter responses are given more weight along with the volume of such incidents, while still meeting system-wide coverage requirements, both customers and providers can benefit from reduced response times. We formulate a model to determine the locations for a given set of ambulances to minimize the system-wide expected response distances while meeting coverage requirements. We solve the model with a heuristic search algorithm and present computational and comparative statistics using data from an existing Emergency Medical Services agency.

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    Article provided by Elsevier in its journal Socio-Economic Planning Sciences.

    Volume (Year): 43 (2009)
    Issue (Month): 4 (December)
    Pages: 253-262

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    Handle: RePEc:eee:soceps:v:43:y:2009:i:4:p:253-262
    Contact details of provider: Web page: http://www.elsevier.com/locate/seps

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    1. J. P. Jarvis, 1985. "Approximating the Equilibrium Behavior of Multi-Server Loss Systems," Management Science, INFORMS, vol. 31(2), pages 235-239, February.
    2. Saydam, Cem & Repede, John & Burwell, Timothy, 1994. "Accurate estimation of expected coverage: A comparative study," Socio-Economic Planning Sciences, Elsevier, vol. 28(2), pages 113-120.
    3. Armann Ingolfsson & Susan Budge & Erhan Erkut, 2008. "Optimal ambulance location with random delays and travel times," Health Care Management Science, Springer, vol. 11(3), pages 262-274, September.
    4. Brotcorne, Luce & Laporte, Gilbert & Semet, Frederic, 2003. "Ambulance location and relocation models," European Journal of Operational Research, Elsevier, vol. 147(3), pages 451-463, June.
    5. R. K. Ahuja & J. B. Orlin & S. Pallottino & M. P. Scaparra & M. G. Scutellà, 2004. "A Multi-Exchange Heuristic for the Single-Source Capacitated Facility Location Problem," Management Science, INFORMS, vol. 50(6), pages 749-760, June.
    6. Saydam, Cem & Aytug, Haldun, 2003. "Accurate estimation of expected coverage: revisited," Socio-Economic Planning Sciences, Elsevier, vol. 37(1), pages 69-80, March.
    7. Rajagopalan, Hari K. & Vergara, F. Elizabeth & Saydam, Cem & Xiao, Jing, 2007. "Developing effective meta-heuristics for a probabilistic location model via experimental design," European Journal of Operational Research, Elsevier, vol. 177(1), pages 83-101, February.
    8. Owen, Susan Hesse & Daskin, Mark S., 1998. "Strategic facility location: A review," European Journal of Operational Research, Elsevier, vol. 111(3), pages 423-447, December.
    9. Zaki, Ahmed S. & Cheng, Hsing Kenneth & Parker, Barnett R., 1997. "A Simulation Model for the Analysis and Management of An Emergency Service System," Socio-Economic Planning Sciences, Elsevier, vol. 31(3), pages 173-189, September.
    10. Aytug, Haldun & Saydam, Cem, 2002. "Solving large-scale maximum expected covering location problems by genetic algorithms: A comparative study," European Journal of Operational Research, Elsevier, vol. 141(3), pages 480-494, September.
    11. ReVelle, Charles, 1989. "Review, extension and prediction in emergency service siting models," European Journal of Operational Research, Elsevier, vol. 40(1), pages 58-69, May.
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