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Locating an Ambulance on the Amherst Campus of the State University of New York at Buffalo

Author

Listed:
  • Yolanda M. Carson

    (Motorola Inc., Elma, New York 14059)

  • Rajan Batta

    (Department of Industrial Engineering, State University of New York at Buffalo, Buffalo, New York 14260)

Abstract

We used past call-data statistics and a network representation of the Amherst Campus of the State University of New York (SUNY) at Buffalo to find a dynamic positioning strategy for the campus ambulance. As measure of performance we used the system-wide average response time to a call. We solved one-median problems on several network “states” to determine optimal locations for the ambulance. To conclude our research effort, we validated the model. Models are not truth—the 30-percent savings predicted by our model evaporated during the test, which gave us a mere six-percent savings. This seemingly odd result is explained by the “elbow” relationship between travel time and travel distance, a relationship empirically postulated by the RAND fire project team in 1979.

Suggested Citation

  • Yolanda M. Carson & Rajan Batta, 1990. "Locating an Ambulance on the Amherst Campus of the State University of New York at Buffalo," Interfaces, INFORMS, vol. 20(5), pages 43-49, October.
    • Handle: RePEc:inm:orinte:v:20:y:1990:i:5:p:43-49
    DOI: 10.1287/inte.20.5.43
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    Citations

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    Cited by:

    1. Batta, Rajan & Lejeune, Miguel & Prasad, Srinivas, 2014. "Public facility location using dispersion, population, and equity criteria," European Journal of Operational Research, Elsevier, vol. 234(3), pages 819-829.
    2. Hoon Jang, 2019. "Designing capacity rollout plan for neonatal care service system in Korea," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 41(3), pages 809-830, September.
    3. Mohammad Yavari & Mohammad Mousavi-Saleh, 2021. "Restructuring hierarchical capacitated facility location problem with extended coverage radius under uncertainty," Operational Research, Springer, vol. 21(1), pages 91-138, March.
    4. Inkyung Sung & Taesik Lee, 2018. "Scenario-based approach for the ambulance location problem with stochastic call arrivals under a dispatching policy," Flexible Services and Manufacturing Journal, Springer, vol. 30(1), pages 153-170, June.
    5. Geroliminis, Nikolas & Kepaptsoglou, Konstantinos & Karlaftis, Matthew G., 2011. "A hybrid hypercube - Genetic algorithm approach for deploying many emergency response mobile units in an urban network," European Journal of Operational Research, Elsevier, vol. 210(2), pages 287-300, April.
    6. Erhan Erkut & Armann Ingolfsson & Güneş Erdoğan, 2008. "Ambulance location for maximum survival," Naval Research Logistics (NRL), John Wiley & Sons, vol. 55(1), pages 42-58, February.
    7. Marković, Nikola & Ryzhov, Ilya O. & Schonfeld, Paul, 2017. "Evasive flow capture: A multi-period stochastic facility location problem with independent demand," European Journal of Operational Research, Elsevier, vol. 257(2), pages 687-703.
    8. Susan Budge & Armann Ingolfsson & Dawit Zerom, 2010. "Empirical Analysis of Ambulance Travel Times: The Case of Calgary Emergency Medical Services," Management Science, INFORMS, vol. 56(4), pages 716-723, April.
    9. 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.
    10. Lim, Gino J. & Sonmez, Ayse Durukan, 2013. "γ-Robust facility relocation problem," European Journal of Operational Research, Elsevier, vol. 229(1), pages 67-74.
    11. T Andersson & P Värbrand, 2007. "Decision support tools for ambulance dispatch and relocation," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(2), pages 195-201, February.

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