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A nested-compliance table policy for emergency medical service systems under relocation

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  • Sudtachat, Kanchala
  • Mayorga, Maria E.
  • Mclay, Laura A.

Abstract

The goal of Emergency Medical Service (EMS) systems is to provide rapid response to emergency calls in order to save lives. This paper proposes a relocation strategy to improve the performance of EMS systems. In practice, EMS systems often use a compliance table to relocate ambulances. A compliance table specifies ambulance base stations as a function of the state of the system. We consider a nested-compliance table, which restricts the number of relocations that can occur simultaneously. We formulate the nested-compliance table model as an integer programming model in order to maximize expected coverage. We determine an optimal nested-compliance table policy using steady state probabilities of a Markov chain model with relocation as input parameters. These parameter approximations are independent of the exact compliance table used. We assume that there is a single type of medical unit, single call priority, and no patient queue. We validate the model by applying the nested-compliance table policies in a simulated system using real-world data. The numerical results show the benefit of our model over a static policy based on the adjusted maximum expected covering location problem (AMEXCLP).

Suggested Citation

  • Sudtachat, Kanchala & Mayorga, Maria E. & Mclay, Laura A., 2016. "A nested-compliance table policy for emergency medical service systems under relocation," Omega, Elsevier, vol. 58(C), pages 154-168.
    • Handle: RePEc:eee:jomega:v:58:y:2016:i:c:p:154-168
    DOI: 10.1016/j.omega.2015.06.001
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    References listed on IDEAS

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    Cited by:

    1. van Barneveld, Thije & Jagtenberg, Caroline & Bhulai, Sandjai & van der Mei, Rob, 2018. "Real-time ambulance relocation: Assessing real-time redeployment strategies for ambulance relocation," Socio-Economic Planning Sciences, Elsevier, vol. 62(C), pages 129-142.
    2. 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.
    3. 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.
    4. Enayati, Shakiba & Mayorga, Maria E. & Rajagopalan, Hari K. & Saydam, Cem, 2018. "Real-time ambulance redeployment approach to improve service coverage with fair and restricted workload for EMS providers," Omega, Elsevier, vol. 79(C), pages 67-80.
    5. Paul, Nicholas R. & Lunday, Brian J. & Nurre, Sarah G., 2017. "A multiobjective, maximal conditional covering location problem applied to the relocation of hierarchical emergency response facilities," Omega, Elsevier, vol. 66(PA), pages 147-158.
    6. Martin van Buuren & Caroline Jagtenberg & Thije van Barneveld & Rob van der Mei & Sandjai Bhulai, 2018. "Ambulance Dispatch Center Pilots Proactive Relocation Policies to Enhance Effectiveness," Interfaces, INFORMS, vol. 48(3), pages 235-246, June.
    7. Yuli Zhang & Amber R. Richter & Jeyaveerasingam George Shanthikumar & Zuo‐Jun Max Shen, 2022. "Dynamic Inventory Relocation in Disaster Relief," Production and Operations Management, Production and Operations Management Society, vol. 31(3), pages 1052-1070, March.
    8. Amir Ali Nasrollahzadeh & Amin Khademi & Maria E. Mayorga, 2018. "Real-Time Ambulance Dispatching and Relocation," Manufacturing & Service Operations Management, INFORMS, vol. 20(3), pages 467-480, July.
    9. Akbari, Leilanaz & Kazemi, Ahmad & Salari, Majid, 2023. "Operational planning of vehicles for rescue and relief operations considering the unavailability of the relocated vehicles," Socio-Economic Planning Sciences, Elsevier, vol. 88(C).
    10. 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.
    11. Yoon, Soovin & Albert, Laura A., 2021. "Dynamic dispatch policies for emergency response with multiple types of vehicles," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 152(C).
    12. Thije van Barneveld, 2016. "The Minimum Expected Penalty Relocation Problem for the Computation of Compliance Tables for Ambulance Vehicles," INFORMS Journal on Computing, INFORMS, vol. 28(2), pages 370-384, May.
    13. Yang, Yun & Ma, Changxi & Ling, Gang, 2022. "Pre-location for temporary distribution station of urban emergency materials considering priority under COVID-19: A case study of Wuhan City, China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 597(C).
    14. Zhou, Lin & Zhen, Lu & Baldacci, Roberto & Boschetti, Marco & Dai, Ying & Lim, Andrew, 2021. "A Heuristic Algorithm for solving a large-scale real-world territory design problem," Omega, Elsevier, vol. 103(C).
    15. 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).

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