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Improving emergency service in rural areas: a bi-objective covering location model for EMS systems

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  • Sunarin Chanta
  • Maria Mayorga
  • Laura McLay

Abstract

Emergency medical service (EMS) systems are public services that often provide the first line of response to urgent health care needs within a community. Unfortunately, it has been widely documented that large disparities in access to care exist between rural and urban communities. While rural EMS is provided through a variety of resources (e.g. air ambulances, volunteer corps, etc.), in this paper we focus on ground ambulatory care. In particular our goal is to balance the level of first-response ambulatory service provided to patients in urban and rural areas by locating ambulances at appropriate stations. In traditional covering location models the objective is to maximize demand that can be covered; consequently, these models favor locating ambulances in more densely populated areas, resulting in longer response times for patients in more rural areas. To address the issue of fairness in semi-rural/semi-urban communities, we propose three bi-objective covering location models that directly consider fairness via a secondary objective. Results are discussed and compared which provide a menu of alternatives to policy makers. Copyright Springer Science+Business Media, LLC 2014

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  • Sunarin Chanta & Maria Mayorga & Laura McLay, 2014. "Improving emergency service in rural areas: a bi-objective covering location model for EMS systems," Annals of Operations Research, Springer, vol. 221(1), pages 133-159, October.
    • Handle: RePEc:spr:annopr:v:221:y:2014:i:1:p:133-159:10.1007/s10479-011-0972-6
    DOI: 10.1007/s10479-011-0972-6
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    Cited by:

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    3. Johanna Guth & Sven Wursthorn & Andreas Ch. Braun & Sina Keller, 2019. "Development of a generic concept to analyze the accessibility of emergency facilities in critical road infrastructure for disaster scenarios: exemplary application for the 2017 wildfires in Chile and ," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 97(3), pages 979-999, July.
    4. Luo, Weicong & Yao, Jing & Mitchell, Richard & Zhang, Xiaoxiang & Li, Wenqiang, 2022. "Locating emergency medical services to reduce urban-rural inequalities," Socio-Economic Planning Sciences, Elsevier, vol. 84(C).
    5. Jaroslav Janáček & Marek Kvet, 2021. "Efficient incrementing heuristics for generalized p-location problems," 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. 29(3), pages 989-1000, September.
    6. 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.
    7. 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).
    8. 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).
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    10. Karatas, Mumtaz & Yakıcı, Ertan, 2019. "An analysis of p-median location problem: Effects of backup service level and demand assignment policy," European Journal of Operational Research, Elsevier, vol. 272(1), pages 207-218.
    11. Nelas, José & Dias, Joana, 2020. "Optimal Emergency Vehicles Location: An approach considering the hierarchy and substitutability of resources," European Journal of Operational Research, Elsevier, vol. 287(2), pages 583-599.
    12. Aleksander Banasik & Argyris Kanellopoulos & G. D. H. Claassen & Jacqueline M. Bloemhof-Ruwaard & Jack G. A. J. Vorst, 2017. "Assessing alternative production options for eco-efficient food supply chains using multi-objective optimization," Annals of Operations Research, Springer, vol. 250(2), pages 341-362, March.
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    14. Fang Zong & Meng Zeng & Yang Cao & Yixuan Liu, 2021. "Local Dynamic Path Planning for an Ambulance Based on Driving Risk and Attraction Field," Sustainability, MDPI, vol. 13(6), pages 1-13, March.

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