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Ambulance location for maximum survival

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  • Erhan Erkut
  • Armann Ingolfsson
  • Güneş Erdoğan

Abstract

This article proposes new location models for emergency medical service stations. The models are generated by incorporating a survival function into existing covering models. A survival function is a monotonically decreasing function of the response time of an emergency medical service (EMS) vehicle to a patient that returns the probability of survival for the patient. The survival function allows for the calculation of tangible outcome measures—the expected number of survivors in case of cardiac arrests. The survival‐maximizing location models are better suited for EMS location than the covering models which do not adequately differentiate between consequences of different response times. We demonstrate empirically the superiority of the survival‐maximizing models using data from the Edmonton EMS system. © 2007 Wiley Periodicals, Inc. Naval Research Logistics, 2008

Suggested Citation

  • Erhan Erkut & Armann Ingolfsson & Güneş Erdoğan, 2008. "Ambulance location for maximum survival," Naval Research Logistics (NRL), John Wiley & Sons, vol. 55(1), pages 42-58, February.
    • Handle: RePEc:wly:navres:v:55:y:2008:i:1:p:42-58
    DOI: 10.1002/nav.20267
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    References listed on IDEAS

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    Cited by:

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    5. 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).
    6. Ľuboš Buzna & Peter Czimmermann, 2021. "On the Modelling of Emergency Ambulance Trips: The Case of the Žilina Region in Slovakia," Mathematics, MDPI, vol. 9(17), pages 1-30, September.
    7. Yiting Xing & Ling Li & Zhuming Bi & Marzena Wilamowska‐Korsak & Li Zhang, 2013. "Operations Research (OR) in Service Industries: A Comprehensive Review," Systems Research and Behavioral Science, Wiley Blackwell, vol. 30(3), pages 300-353, May.
    8. Ľudmila Jánošíková & Marek Kvet & Peter Jankovič & Lýdia Gábrišová, 2019. "An optimization and simulation approach to emergency stations relocation," 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. 27(3), pages 737-758, September.
    9. Nilay Noyan, 2010. "Alternate risk measures for emergency medical service system design," Annals of Operations Research, Springer, vol. 181(1), pages 559-589, December.
    10. Iannoni, Ana P. & Morabito, Reinaldo, 2023. "A review on hypercube queuing model's extensions for practical applications," Socio-Economic Planning Sciences, Elsevier, vol. 89(C).
    11. Farahani, Reza Zanjirani & Fallah, Samira & Ruiz, Rubén & Hosseini, Sara & Asgari, Nasrin, 2019. "OR models in urban service facility location: A critical review of applications and future developments," European Journal of Operational Research, Elsevier, vol. 276(1), pages 1-27.
    12. Akdogan, M. Altan & Bayındır, Z. Pelin & Iyigun, Cem, 2023. "An analysis of ambulance location problem from an equity perspective," Socio-Economic Planning Sciences, Elsevier, vol. 90(C).
    13. Yiping Jiang & Yufei Yuan, 2019. "Emergency Logistics in a Large-Scale Disaster Context: Achievements and Challenges," IJERPH, MDPI, vol. 16(5), pages 1-23, March.
    14. Suriyaphong Nilsang & Chumpol Yuangyai & Chen-Yang Cheng & Udom Janjarassuk, 2019. "Locating an ambulance base by using social media: a case study in Bangkok," Annals of Operations Research, Springer, vol. 283(1), pages 497-516, December.
    15. Matinrad, Niki & Granberg, Tobias Andersson, 2023. "Optimal pre-dispatch task assignment of volunteers in daily emergency response," Socio-Economic Planning Sciences, Elsevier, vol. 87(PB).
    16. 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).
    17. 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.
    18. Yoon, Soovin & Albert, Laura A., 2020. "A dynamic ambulance routing model with multiple response," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 133(C).
    19. Wang, Wei & Wu, Shining & Wang, Shuaian & Zhen, Lu & Qu, Xiaobo, 2021. "Emergency facility location problems in logistics: Status and perspectives," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 154(C).
    20. 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).
    21. Niki Matinrad & Melanie Reuter-Oppermann, 2022. "A review on initiatives for the management of daily medical emergencies prior to the arrival of emergency medical services," 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. 30(1), pages 251-302, March.
    22. 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.
    23. Timothy C. Y. Chan & Derya Demirtas & Roy H. Kwon, 2016. "Optimizing the Deployment of Public Access Defibrillators," Management Science, INFORMS, vol. 62(12), pages 3617-3635, December.

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