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


  • Rajagopalan, Hari K.
  • Saydam, Cem


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.

Suggested Citation

  • Rajagopalan, Hari K. & Saydam, Cem, 2009. "A minimum expected response model: Formulation, heuristic solution, and application," Socio-Economic Planning Sciences, Elsevier, vol. 43(4), pages 253-262, December.
  • Handle: RePEc:eee:soceps:v:43:y:2009:i:4:p:253-262

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    References listed on IDEAS

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    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
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    1. repec:pal:jorsoc:v:68:y:2017:i:7:d:10.1057_jors.2016.39 is not listed on IDEAS
    2. Dirk Degel & Lara Wiesche & Sebastian Rachuba & Brigitte Werners, 2015. "Time-dependent ambulance allocation considering data-driven empirically required coverage," Health Care Management Science, Springer, vol. 18(4), pages 444-458, December.
    3. Kusumastuti, Ratih Dyah & Wibowo, Sigit Sulistiyo & Insanita, Rizqiah, 2010. "Hierarchical modeling approach for relief logistics nework Design," MPRA Paper 41089, University Library of Munich, Germany.
    4. Cheng, Yung-Hsiang & Liang, Zheng-Xian, 2014. "A strategic planning model for the railway system accident rescue problem," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 69(C), pages 75-96.
    5. Shariat-Mohaymany, Afshin & Babaei, Mohsen & Moadi, Saeed & Amiripour, Sayyed Mahdi, 2012. "Linear upper-bound unavailability set covering models for locating ambulances: Application to Tehran rural roads," European Journal of Operational Research, Elsevier, vol. 221(1), pages 263-272.


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