Simulation of dispatching and allocation of ambulances for emergency medical services in urban areas

Document Type : Research/ Original/ Regular Article

Authors

1 Associate Professor, Industrial Engineering, Islamic Azad University, Qazvin Branch

2 Ph.D. Student in Operations Research, Allameh Tabataba’i University

Abstract

Emergency Medical Service (EMS) Systems provide pre-hospital emergency care services to patients and injured. Needed time in order to respond to an emergency call (arrival time to the scene of the accident) is one of the most important aspects of EMS service facility. Optimal planning of resources for rapid response has been made difficult due to the random number of ambulance requests and operational constraints. This difficulty is particularly important in the dispatching and allocation of ambulances in the urban area.
In the present study, simulation modeling has been used to plan the dispatching and allocation of ambulances in the urban area. In order to achieve rapid response, the modeling prioritizes emergency calls to cover specific patients, such as heart patients, in the shortest possible time. The study has used ED simulation software for dispatching modeling of emergency servers at a city level with different demand points and random distribution functions for demand and service rate of servers. In this regard, two scenarios have been developed that have been repeated to the required number and then compared using statistical methods according to performance criteria such as patients' probability of survival, mean response time and service time. In this study, the best-case scenario is that by deploying a variable server at each base in order to response emergency, it improves responsiveness and performance measures.

Keywords

References

[1] Goldberg, J. (2004). Operation research models for the deployment of emergency services vehicles. EMS Management Journal, 1(1), 20-39.
[2] Aringhieri, R., Carello, G., & Morale, D. (2007). Ambulance location through optimization and simulation: the case of Milano urban area. Univerity of Milano, Italy.
[3] Billhardt, H., Lujak, M., Sanchez-Brunete, V., Fernandez, A., & Ossowski, S. (2014). Dynamic coordination of ambulances for emergency medical assistance services. Knowledge-Based Systems, 70, 268-280.
[4] zhen, L., Wang, K., Hu, H., & Chang, D. (2014). simulation optimization framework for ambulance deployment and relocation problems. Computers & Industrial Engineering, 72, 12-23.
[5] Ebrahimi, M., & Mirzaei Modam, M. (2015). Ranking Zones of Tehran to Add New Emergency Services Using Fuzzy AHP. Journal of Industrial Engineering, University of Tehran, 49(2), 149-163.
[6] Shiah, D. M., & & Chen, S. W. (2007). Ambulance allocation capacity model. 9th International Conference on e-Health Networking, Application and Services (pp. 40-45). Taipei: IEEE.
[7] Brotcorne, L., Laporte, G., & Semet, F. (2003). Ambulance location and relocation models. European Journal of Operational Research, 147, 451–463.
[8] Nordin, N., Zaharudin, Z., & Maasar, M. (2012). Finding shortest path of the ambulance routing: Interface of A∗ algorithm using C# programming. Symposium on Humanities. Science and Engineering Research, 1569-1573.
[9] Wang, Y., Luangkesorn, K., & Shuman, L. (2012). Modeling emergency medical response to a mass casualty incident using agent based simulation. Socio-Economic Planning Sciences, 46(4), 281-290.
[10] erlin, G. N., & Liebman, J. C. (1974). Mathematical analysis of emergency ambulance location. Socio-Economic Planning Sciences, 8(6), 323-328.
[11] Van Essen, J., Hurink, J., Nickel, S., & Reuter, M. (2013). Models for ambulance planning on the strategic and the tactical level. Beta working paper series.
[12] Aringhieri, R., Bruni, M., Khodaparasti, S., & van Essen, J. (2017). Emergency medical services and beyond: Addressing new challenges. Computers & Operations Research, 78, 349-368.
[13] م. امیری, م. علی پور و م. حیدری فرسنگی, “الگوریتم های ژنتیک و ممتیک برای مدل صف فازی حداکثر پوشش مکان یابی-تخصیص با در نظرگرفتن تراکم در سیستم و چند نوع تقاضا,” مهندسی صنایع و مدیریت شریف, جلد 2, 15-25, 1390.
[14] Abo-Hamad, W., & Arisha, A. (2013). Simulation-based framework to improve patient experience in an emergency department. European Journal of Operational Research, 224, 154-166.
[15] Laporte, G., Nickel, S., & Saldanha da Gama, F. (2015). Location Science. Springer International Publishing.
[16] S. Syam, S. (2008). A multiple server location–allocation model for service system design. Computers & Operations Research, 35, 2248 – 2265.
[17] ح. شاه بندرزاده و م. منصوری, “مدل ریاضی فازی مکان یابی تخصیص سلسله مراتبی برای خدمات فوریت های پزشکی با به کارگیری الگوریتم NSGA-II,” در دومین کنفرانس بین المللی مدیریت صنعتی, تهران, 1396.
[18] Alsalloum, O., & Rand, G. (2006). Extensions to emergency vehicle location models. Computers & Operations Research, 33, 2725-2743.
[19] Zarkeshzadeh, M., Zare, H., Heshmati, Z., & Teimouri, M. (2016). A novel hybrid method for improving ambulance dispatching response time through a simulation study. Simulation Modelling Practice and Theory, 60, 170–184.
[20] McCormack, R., & Coates, G. (2015). A simulation model to enable the optimization of ambulance fleet allocation and base station location for increased patient survival. European Journal of Operational Research, 247, 294-309.
[21] Lee, T., Jang, H., Cho, S., & Turner, J. (2012). A simulation-based iterative method for trauma center–air ambulance location problem. Proceedings of the 2012 Winter Simulation Conference, (pp. 1-12). Berlin.
[22] Ünlüyurt , T., & Tunçer, a. (2016). Estimating the performance of emergency medical service location models via discrete event simulation. Computers & Industrial Engineering, 102, 467-475 [6] [23] Anagnostou, A., Nouman, A., & J.E. Taylor, S. (2013). DISTRIBUTED HYBRID AGENT-BASED DISCRETE EVENT EMERGENCY MEDICAL SERVICES SIMULATION. Proceedings of the 2013 Winter Simulation Conference (pp. 1625-1636). IEEE.
[24] خ. سلیمی فرد و م. منصوری, “مکانیابی و تخصیص آمبولانس با رویکرد آمیخته تیوری صف (شبیه سازی) و برنامه ریزی عدد صحیح,” در دومین کنفرانس بین المللی تحولات نوین در مدیریت ، اقتصاد و حسابداری, تهران, 1397.
[25] Chanta, S., E. Mayorga, M., & A. McLay, L. (2014). The minimum p-envy location problem with requirement on minimum survival rate. Computers & Industrial Engineering, 74, 228-239.
[26] Erkut, E., Ingolfsson, A., & Erdogan, G. (2007). Ambulance location for maximum survival. Naval Research Logistics, 55, 42-58.
[27] Larsen, M., Eisenberg, M., Cummins, R., & Hallstrom, A. (1993). Predicting survival from out-of-hospital cardiacarrest—A graphic model. Annals of Emergency Medicine, 22(11), 1652–1658.
[28] Waalewijn , R., De Vos , R., Tijssen , J., & Koster , R. (2001). Survival models for out-of-hospital cardiopulmonary resuscitation from the perspectives of the bystander, the first responder, and the paramedic. Resuscitation , 51(22), 113-122.
[29] De Maio, V., G. Stiell, I., A. Wells, G., & W. Spaite, D. (2003). Optimal defibrillation response intervals for maximum out-of-hospital cardiac arrest survival rates. Annals of Emergency Medicine, 42(2), 242–250.
[30] Knight, V., Harper, P., & Smith, L. (2012). Ambulance allocation for maximal survival with heterogeneous outcome measures. Omega, 40, 918-926.
[31] Kanchala, S., Mayorga, M., & McLay, L. (2014). Recommendations for dispatching emergency vehicles under multi tiered response via simulation. International Transactions in Operation Research, 21(4), 581-617.
Supply Chain Management
Volume 22, Issue 67
October 2020
Pages 67-79

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History

  • Receive Date: 04 May 2020
  • Revise Date: 09 July 2020
  • Accept Date: 26 July 2020

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