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A Methodology for Emergency Ambulance Deployment

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  • James A. Fitzsimmons

    (University of Texas)

Abstract

An important feature of emergency ambulance systems is that the probability of any given unit being busy is dependent upon which of the other units are busy. An analytical model incorporating this feature is developed and found to be reliable in predicting the entire response time distribution for an actual operating system. A computer version of the model coupled with a pattern search routine constitutes the ambulance deployment methodology referred to as CALL (Computerized Ambulance Location Logic). This methodology may be used to find the deployment of ambulances that minimizes mean response time, as illustrated by an application for the City of Los Angeles. CALL was used to select from a possible set of 34 firehouses 14 for the location of emergency ambulances. Although the objective was the minimization of mean response time, the final deployment substantially reduced the probability of excessive response times as well. Another ancillary benefit from the use of CALL was a substantial smoothing of workload among the 14 ambulance crews.

Suggested Citation

  • James A. Fitzsimmons, 1973. "A Methodology for Emergency Ambulance Deployment," Management Science, INFORMS, vol. 19(6), pages 627-636, February.
    • Handle: RePEc:inm:ormnsc:v:19:y:1973:i:6:p:627-636
    DOI: 10.1287/mnsc.19.6.627
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    Cited by:

    1. M S Daskin & A Haghani, 1984. "Multiple Vehicle Routing and Dispatching to an Emergency Scene," Environment and Planning A, , vol. 16(10), pages 1349-1359, October.
    2. Linda V. Green & Peter J. Kolesar, 2004. "ANNIVERSARY ARTICLE: Improving Emergency Responsiveness with Management Science," Management Science, INFORMS, vol. 50(8), pages 1001-1014, August.
    3. Rettke, Aaron J. & Robbins, Matthew J. & Lunday, Brian J., 2016. "Approximate dynamic programming for the dispatch of military medical evacuation assets," European Journal of Operational Research, Elsevier, vol. 254(3), pages 824-839.
    4. Sun Hoon Kim & Young Hoon Lee, 2016. "Iterative optimization algorithm with parameter estimation for the ambulance location problem," Health Care Management Science, Springer, vol. 19(4), pages 362-382, December.
    5. P. Daniel Wright & Matthew J. Liberatore & Robert L. Nydick, 2006. "A Survey of Operations Research Models and Applications in Homeland Security," Interfaces, INFORMS, vol. 36(6), pages 514-529, December.
    6. Sorensen, Paul & Church, Richard, 2010. "Integrating expected coverage and local reliability for emergency medical services location problems," Socio-Economic Planning Sciences, Elsevier, vol. 44(1), pages 8-18, March.
    7. Colla, Marcos & Santos, Gilson Ditzel & Oliveira, Gilson Adamczuk & de Vasconcelos, Renata Braga Berenguer, 2023. "Ambulance response time in a Brazilian emergency medical service," Socio-Economic Planning Sciences, Elsevier, vol. 85(C).
    8. Gerald G. Brown & Antonios L. Vassiliou, 1993. "Optimizing disaster relief: Real‐time operational and tactical decision support," Naval Research Logistics (NRL), John Wiley & Sons, vol. 40(1), pages 1-23, February.
    9. Amini, M. Mehdi & Retzlaff-Roberts, Donna & Bienstock, Carol C., 2005. "Designing a reverse logistics operation for short cycle time repair services," International Journal of Production Economics, Elsevier, vol. 96(3), pages 367-380, June.
    10. Berman, Oded & Krass, Dmitry & Menezes, Mozart B.C., 2013. "Location and reliability problems on a line: Impact of objectives and correlated failures on optimal location patterns," Omega, Elsevier, vol. 41(4), pages 766-779.
    11. Repoussis, Panagiotis P. & Paraskevopoulos, Dimitris C. & Vazacopoulos, Alkiviadis & Hupert, Nathaniel, 2016. "Optimizing emergency preparedness and resource utilization in mass-casualty incidents," European Journal of Operational Research, Elsevier, vol. 255(2), pages 531-544.
    12. N C Simpson & P G Hancock, 2009. "Fifty years of operational research and emergency response," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(1), pages 126-139, May.
    13. Shayesta Wajid & N. Nezamuddin, 2023. "Optimizing emergency services for road safety using a decomposition method: a case study of Delhi," OPSEARCH, Springer;Operational Research Society of India, vol. 60(1), pages 155-173, March.
    14. Gad Allon & Sarang Deo & Wuqin Lin, 2013. "The Impact of Size and Occupancy of Hospital on the Extent of Ambulance Diversion: Theory and Evidence," Operations Research, INFORMS, vol. 61(3), pages 544-562, June.

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