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Emergency ambulance deployment in Barbados: a multi-objective approach

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  • S I Harewood

    (University of the West Indies)

Abstract

A multi-objective version of the Maximum Availability Location Problem is presented in this paper. The assumption of server independence is relaxed by adopting the approach of the Queuing Probabilistic Location Set Covering Problem for calculating the probability that all servers in a given region are busy. The first objective seeks to maximize the population receiving coverage within a given distance standard and with a given level of reliability. The second objective chooses those locations which minimize the cost of covering the population. This model is used to obtain sets of good locations using data obtained from the Barbados Emergency Ambulance Service. The solutions obtained from the optimization model are then subject to a detailed analysis by simulation. The results reveal the potentially good performance of the system, when locations derived from the optimization model are used.

Suggested Citation

  • S I Harewood, 2002. "Emergency ambulance deployment in Barbados: a multi-objective approach," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 53(2), pages 185-192, February.
    • Handle: RePEc:pal:jorsoc:v:53:y:2002:i:2:d:10.1057_palgrave.jors.2601250
    DOI: 10.1057/palgrave.jors.2601250
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    Citations

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    Cited by:

    1. Bélanger, V. & Ruiz, A. & Soriano, P., 2019. "Recent optimization models and trends in location, relocation, and dispatching of emergency medical vehicles," European Journal of Operational Research, Elsevier, vol. 272(1), pages 1-23.
    2. A Ingolfsson & E Erkut & S Budge, 2003. "Simulation of single start station for Edmonton EMS," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 54(7), pages 736-746, July.
    3. 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).
    4. Sophie N. Parragh & Fabien Tricoire & Walter J. Gutjahr, 2022. "A branch-and-Benders-cut algorithm for a bi-objective stochastic facility location problem," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 44(2), pages 419-459, June.
    5. F Silva & D Serra, 2008. "Locating emergency services with different priorities: the priority queuing covering location problem," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(9), pages 1229-1238, September.
    6. Ying Ji & Jianhui Du & Xiaoqing Wu & Zhong Wu & Deqiang Qu & Dan Yang, 2021. "Robust optimization approach to two-echelon agricultural cold chain logistics considering carbon emission and stochastic demand," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(9), pages 13731-13754, September.
    7. Ana Iannoni & Reinaldo Morabito & Cem Saydam, 2008. "A hypercube queueing model embedded into a genetic algorithm for ambulance deployment on highways," Annals of Operations Research, Springer, vol. 157(1), pages 207-224, January.
    8. Amin Akbari & Ronald Pelot & H. A. Eiselt, 2018. "A modular capacitated multi-objective model for locating maritime search and rescue vessels," Annals of Operations Research, Springer, vol. 267(1), pages 3-28, August.
    9. Azrah Anparasan & Miguel Lejeune, 2019. "Resource deployment and donation allocation for epidemic outbreaks," Annals of Operations Research, Springer, vol. 283(1), pages 9-32, December.
    10. 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.
    11. Xueping Li & Zhaoxia Zhao & Xiaoyan Zhu & Tami Wyatt, 2011. "Covering models and optimization techniques for emergency response facility location and planning: a review," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 74(3), pages 281-310, December.
    12. Atkinson, J.B. & Kovalenko, I.N. & Kuznetsov, N. & Mykhalevych, K.V., 2008. "A hypercube queueing loss model with customer-dependent service rates," European Journal of Operational Research, Elsevier, vol. 191(1), pages 223-239, November.
    13. repec:spr:compst:v:74:y:2011:i:3:p:281-310 is not listed on IDEAS
    14. A Başar & B Çatay & T Ünlüyurt, 2011. "A multi-period double coverage approach for locating the emergency medical service stations in Istanbul," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(4), pages 627-637, April.

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