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Locating Emergency Vehicle Bases When Service Time Depends on Call Location

Author

Listed:
  • Jeffrey Goldberg

    (University of Arizona, Tucson, Arizona 85721)

  • Luis Paz

    (University of Arizona, Tucson, Arizona 85721)

Abstract

This paper deals with the problem of locating emergency vehicles in an urban area. We formulate an optimization model that extends previous work by allowing stochastic travel times, unequal vehicle utilizations, various call types, and service times that depend on call location. The basis of the model is a procedure for approximating the performance of spatially distributed queueing systems. In previous work the model has been validated using data from the Tucson Emergency Medical Services (EMS). We test the computational effectiveness of pairwise interchange heuristics on 192 test problems. Demand and service time components of the test data are generated using characteristics of the Tucson data set. For these test problems, simple pairwise interchange techniques yield reasonable solutions with little computational effort. Also, solutions obtained from the model differ from those generated using methods previously presented in the literature.

Suggested Citation

  • Jeffrey Goldberg & Luis Paz, 1991. "Locating Emergency Vehicle Bases When Service Time Depends on Call Location," Transportation Science, INFORMS, vol. 25(4), pages 264-280, November.
    • Handle: RePEc:inm:ortrsc:v:25:y:1991:i:4:p:264-280
    DOI: 10.1287/trsc.25.4.264
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    Citations

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    Cited by:

    1. Susana Baptista & Rui Oliveira, 2012. "A case study on the application of an approximated hypercube model to emergency medical systems management," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 20(4), pages 559-581, December.
    2. Wang, Wei & Wang, Shuaian & Zhen, Lu & Qu, Xiaobo, 2022. "EMS location-allocation problem under uncertainties," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 168(C).
    3. Bélanger, V. & Lanzarone, E. & Nicoletta, V. & Ruiz, A. & Soriano, P., 2020. "A recursive simulation-optimization framework for the ambulance location and dispatching problem," European Journal of Operational Research, Elsevier, vol. 286(2), pages 713-725.
    4. Wajid, Shayesta & Nezamuddin, N., 2023. "Capturing delays in response of emergency services in Delhi," Socio-Economic Planning Sciences, Elsevier, vol. 87(PA).
    5. Rautenstrauss, Maximiliane & Martin, Layla & Minner, Stefan, 2023. "Ambulance dispatching during a pandemic: Tradeoffs of categorizing patients and allocating ambulances," European Journal of Operational Research, Elsevier, vol. 304(1), pages 239-254.
    6. 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.
    7. Sukanya Samanta & Goutam Sen & Soumya Kanti Ghosh, 2022. "A literature review on police patrolling problems," Annals of Operations Research, Springer, vol. 316(2), pages 1063-1106, September.
    8. G Erdoğan & E Erkut & A Ingolfsson & G Laporte, 2010. "Scheduling ambulance crews for maximum coverage," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(4), pages 543-550, April.
    9. Bonneu, Florent & Thomas-Agnan, Christine, 2009. "Spatial point process models for location-allocation problems," Computational Statistics & Data Analysis, Elsevier, vol. 53(8), pages 3070-3081, June.
    10. Mateo Restrepo & Shane Henderson & Huseyin Topaloglu, 2009. "Erlang loss models for the static deployment of ambulances," Health Care Management Science, Springer, vol. 12(1), pages 67-79, March.
    11. KC, Kiran & Corcoran, Jonathan & Chhetri, Prem, 2020. "Measuring the spatial accessibility to fire stations using enhanced floating catchment method," Socio-Economic Planning Sciences, Elsevier, vol. 69(C).
    12. Soovin Yoon & Laura A. Albert & Veronica M. White, 2021. "A Stochastic Programming Approach for Locating and Dispatching Two Types of Ambulances," Transportation Science, INFORMS, vol. 55(2), pages 275-296, March.
    13. 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.
    14. Leknes, Håkon & Aartun, Eirik Skorge & Andersson, Henrik & Christiansen, Marielle & Granberg, Tobias Andersson, 2017. "Strategic ambulance location for heterogeneous regions," European Journal of Operational Research, Elsevier, vol. 260(1), pages 122-133.
    15. 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.
    16. DuBois, Eric & Schmidt, Adam & Albert, Laura A., 2021. "Location of trauma care resources with inter-facility patient transfers," Operations Research Perspectives, Elsevier, vol. 8(C).

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