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Methods for Allocating Urban Emergency Units: A Survey

Author

Listed:
  • Jan M. Chaiken

    (New York City-Rand Institute)

  • Richard C. Larson

    (Massachusetts Institute of Technology)

Abstract

An urban emergency service system provides mobile units (vehicles) to respond to requests for service which can occur at any time and any place throughout a city. This paper describes the common characteristics and operational problems of these systems and surveys the various methods, both traditional and recently developed, which may be used for allocating their units. Aspects of allocation policy discussed include (1) determining the number of units to have on duty, (2) locating the units, (3) designing their response areas or patrol areas, (4) relocating units, and (5) planning preventive-patrol patterns for police cars. Typical policy changes which may be suggested by the use of quantitative allocation models include selective queuing of low priority calls, varying the number of units on duty (and their locations) by time of day, dispatching units other than the closest ones to certain incidents, relocating units as unavailabilities begin to develop, and assigning police cars to overlapping patrol sectors. As a result of making such changes, it is often possible to reduce queuing and travel time delays, improve the balance of workload among units, and enhance the amount of preventive patrol where needed.

Suggested Citation

  • Jan M. Chaiken & Richard C. Larson, 1972. "Methods for Allocating Urban Emergency Units: A Survey," Management Science, INFORMS, vol. 19(4-Part-2), pages 110-130, December.
    • Handle: RePEc:inm:ormnsc:v:19:y:1972:i:4-part-2:p:p110-p130
    DOI: 10.1287/mnsc.19.4.P110
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    Cited by:

    1. Phillip R. Jenkins & Matthew J. Robbins & Brian J. Lunday, 2021. "Approximate Dynamic Programming for Military Medical Evacuation Dispatching Policies," INFORMS Journal on Computing, INFORMS, vol. 33(1), pages 2-26, January.
    2. Doeksen, Gerald A. & Oehrtman, Robert L., 1976. "Optimum Locations For A Rural Fire System: A Study Of Major County, Oklahoma," Southern Journal of Agricultural Economics, Southern Agricultural Economics Association, vol. 8(2), pages 1-7, December.
    3. Michael J. Fry & Michael J. Magazine & Uday S. Rao, 2006. "Firefighter Staffing Including Temporary Absences and Wastage," Operations Research, INFORMS, vol. 54(2), pages 353-365, April.
    4. 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.
    5. Knight, V.A. & Harper, P.R. & Smith, L., 2012. "Ambulance allocation for maximal survival with heterogeneous outcome measures," Omega, Elsevier, vol. 40(6), pages 918-926.
    6. Juan Rivera & H. Afsar & Christian Prins, 2015. "A multistart iterated local search for the multitrip cumulative capacitated vehicle routing problem," Computational Optimization and Applications, Springer, vol. 61(1), pages 159-187, May.
    7. Almehdawe, Eman & Jewkes, Beth & He, Qi-Ming, 2013. "A Markovian queueing model for ambulance offload delays," European Journal of Operational Research, Elsevier, vol. 226(3), pages 602-614.
    8. Desheng Dash Wu & Jia Liu & David L. Olson, 2015. "Simulation Decision System on the Preparation of Emergency Resources Using System Dynamics," Systems Research and Behavioral Science, Wiley Blackwell, vol. 32(6), pages 603-615, November.
    9. Hye-Seung Han & Eunsung Oh & Sung-Yong Son, 2018. "Study on EV Charging Peak Reduction with V2G Utilizing Idle Charging Stations: The Jeju Island Case," Energies, MDPI, vol. 11(7), pages 1-13, June.
    10. 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.
    11. Richard Charles Larson, 2002. "Public Sector Operations Research: A Personal Journey," Operations Research, INFORMS, vol. 50(1), pages 135-145, February.
    12. 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.
    13. Altay, Nezih & Green III, Walter G., 2006. "OR/MS research in disaster operations management," European Journal of Operational Research, Elsevier, vol. 175(1), pages 475-493, November.
    14. Jenkins, Phillip R. & Lunday, Brian J. & Robbins, Matthew J., 2020. "Robust, multi-objective optimization for the military medical evacuation location-allocation problem," Omega, Elsevier, vol. 97(C).
    15. 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.
    16. 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.
    17. Seokcheon Lee, 2017. "A new preparedness policy for EMS logistics," Health Care Management Science, Springer, vol. 20(1), pages 105-114, March.
    18. Xian Cheng & Shaoyi Liao & Zhongsheng Hua, 2017. "A policy of picking up parcels for express courier service in dynamic environments," International Journal of Production Research, Taylor & Francis Journals, vol. 55(9), pages 2470-2488, May.
    19. Shixiang Zhu & He Wang & Yao Xie, 2022. "Data-Driven Optimization for Atlanta Police-Zone Design," Interfaces, INFORMS, vol. 52(5), pages 412-432, September.

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