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Joint ground and air emergency medical services coverage models: A greedy heuristic solution approach

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  • Erdemir, Elif Tokar
  • Batta, Rajan
  • Rogerson, Peter A.
  • Blatt, Alan
  • Flanigan, Marie

Abstract

Aeromedical and ground ambulance services often team up in responding to trauma crashes, especially when the emergency helicopter is unable to land at the crash scene. We propose location-coverage models and a greedy heuristic for their solution to simultaneously locate ground and air ambulances, and landing zones (transfer points). We provide a coverage definition based on both response time and total service time, and consider three coverage options; only ground emergency medical services (EMS) coverage, only air EMS coverage, or joint coverage of ground and air EMS in which the patient is transferred from an ambulance into an emergency helicopter at a transfer point. To analyze this complex coverage situation we develop two sets of models, which are variations of the Location Set Covering Problem (LSCP) and the Maximal Covering Location Problem (MCLP). These models address uncertainty in spatial distribution of motor vehicle crash locations by providing coverage to a given set of both crash nodes and paths. The models also consider unavailability of ground ambulances by drawing upon concepts from backup coverage models. We illustrate our results on a case study that uses crash data from the state of New Mexico. The case study shows that crash node and path coverage percentage values decrease when ground ambulances are utilized only within their own jurisdiction.

Suggested Citation

  • Erdemir, Elif Tokar & Batta, Rajan & Rogerson, Peter A. & Blatt, Alan & Flanigan, Marie, 2010. "Joint ground and air emergency medical services coverage models: A greedy heuristic solution approach," European Journal of Operational Research, Elsevier, vol. 207(2), pages 736-749, December.
    • Handle: RePEc:eee:ejores:v:207:y:2010:i:2:p:736-749
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    References listed on IDEAS

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    6. Ran Wei, 2016. "Coverage Location Models," International Regional Science Review, , vol. 39(1), pages 48-76, January.
    7. Baofeng Sun & Jiaojiao Liu & Junyi Hao & Xiuxiu Shen & Xinhua Mao & Xianmin Song, 2020. "Maintenance Decision-Making of an Urban Rail Transit System in a Regionalized Network-Wide Perspective," Sustainability, MDPI, vol. 12(22), pages 1-21, November.
    8. 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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    11. Vicencio-Medina, Salvador J. & Rios-Solis, Yasmin A. & Ibarra-Rojas, Omar Jorge & Cid-Garcia, Nestor M. & Rios-Solis, Leonardo, 2023. "The maximal covering location problem with accessibility indicators and mobile units," Socio-Economic Planning Sciences, Elsevier, vol. 87(PB).
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    13. Liu, Shaonan & Kong, Nan & Parikh, Pratik & Wang, Mingzheng, 2023. "Optimal trauma care network redesign with government subsidy: A bilevel integer programming approach," Omega, Elsevier, vol. 119(C).
    14. 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.
    15. Ranon Jientrakul & Chumpol Yuangyai & Klongkwan Boonkul & Pakinai Chaicharoenwut & Suriyaphong Nilsang & Sittiporn Pimsakul, 2022. "Integrating Spatial Risk Factors with Social Media Data Analysis for an Ambulance Allocation Strategy: A Case Study in Bangkok," Sustainability, MDPI, vol. 14(16), pages 1-15, August.
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    18. Ran Wei & Alan Murray & Rajan Batta, 2014. "A bounding-based solution approach for the continuous arc covering problem," Journal of Geographical Systems, Springer, vol. 16(2), pages 161-182, April.

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