Introduction
Due to the large number of injured people after disasters, most hospitals and medical centers do not have enough capacity to provide services to them. In addition, in disasters such as floods and earthquakes, most hospitals and medical centers located in the affected regions are often destroyed and become impossible to use. Therefore, the establishment of mobile medical units, which are known as field hospitals, can partially solve the lack of capacity and the inability to provide local medical services. However, it should be noted that field hospitals are useful if they become available and on-site within the first days after the disaster to prevent high casualties and carry out rescue operations faster with better quality. Another important point is the location of field hospitals. The time of transferring the injured from the affected areas to these facilities increases, If they are built at a far distance from the affected areas, which can increase the casualties. Therefore, this study aims to determine the appropriate location of field hospitals. The main questions in this paper are as follows:
• How many field hospitals are needed in Amol city and where should they be established?
• How to use the available resources to provide medical services to more people?
• How much is the effect of the budget on the quality of services provided to the injured people?
Methods
This paper provides a mathematical modeling of the problem of selecting location for field hospitals and allocation of resources after the earthquake. In this regard, we used the real data of Amol City located in Mazandaran province, Iran, to evaluate the proposed model. After collecting the required data, the proposed model was implemented in GAMS 25.1 optimization software and with the GUROBI solver.
Results
Field hospitals include different types, three of which are: tent, container, and a mix of both. The main differences between these hospitals are the cost of their construction and their capacity (number of beds). This article assumed the first, second, and third types of field hospitals had 30, 60, and 90 beds, respectively. The optimal place for constructing a field hospital was Banafsheh Park, and this field hospital should be of the second type (container).
Conclusion
Nowadays, the world is facing an increase in the number and variety of disasters due to climate, biological, and technological changes. Among the challenges that arise after a disaster, the selection of a optimal location for field hospitals and proper allocation of medical resources are of great importance. For this purpose, an integer programming model was proposed in this study with various constraints, including constraints in the budget for the construction , the capacity of hospitals to admit victims, the number of ambulances, the number of medical staff, and the amount of available medications. The results of this study can help medical centers to make the best decisions regarding how to use their limited available resources after a disaster.
Ethical Considerations
Compliance with ethical guidelines
All ethical principles were observed in this study . All participants were informed about the study objectives and methods.
Funding
This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors
Authors' contributions
All authors equally contributed to preparing this article.
Conflicts of interest
The authors declare no conflict of interest.
References
Akhtari, F., & Noorizadeh, R. (2006). [Field hospitals and their role in natural disasters (Persian)]. Paper presented at: 3rd International Congress on Health, Medication and Crisis Management in Disaster, Tehran, Iran, 22 November 2006. [Link]
Atash Panjeh, H., Dast Dadeh, F., & Porbin, Z. (2016). [Investigation of field hospitals from the point of view of passive defense (Persian)]. Paper presented at: National Conference on Passive Defense and Sustainable Development, Tehran, Iran, 15 October 2016. [Link]
Gitashenasi. (2023). [Atlas of Tehran (Persian)]. Retrieved from: [Link]
Liu, M., & Liang, J. (2013). Dynamic optimization model for allocating medical resources in epidemic controlling. Journal of Industrial Engineering and Management, 6(1), 73-88. [DOI:10.3926/jiem.663]
Moladavoodi, H., Paydar, M. M., & Safai, A. (2016). [Locating field hospitals with the approach of hierarchical analysis process (Persian)]. Paper presented at: The International Conference in New Research of Industry and Mechanical Engineering, Tehran, Iran, 16 February 2016. [Link]
Zahiri, B., Tavakkoli-Moghaddam, R., & Pishvaee, M. S. (2014). A robust possibilistic programming approach to multi-period location-allocation of organ transplant centers under uncertainty. Computers & Industrial Engineering, 74, 139-148. [DOI:10.1016/j.cie.2014.05.008]
Ko, Y. D., Song, B. D., & Hwang, H. (2016). Location, capacity and capability design of emergency medical centers with multiple emergency diseases. Computers & Industrial Engineering, 101, 10-20. [DOI:10.1016/j.cie.2016.08.011]
Sun, L., DePuy, G. W., & Evans, G. W. (2014). Multi-objective optimization models for patient allocation during a pandemic influenza outbreak. Computers & Operations Research, 51, 350-359. [DOI:10.1016/j.cor.2013.12.001]
Kasaie, P., & Kelton, W. D. (2013). Simulation optimization for allocation of epidemic-control resources. IIE Transactions on Healthcare Systems Engineering, 3(2), 78-93. [DOI:10.1080/19488300.2013.790717]
Dellaert, N., Cayiroglu, E., & Jeunet, J. (2016). Assessing and controlling the impact of hospital capacity planning on the waiting time. International Journal of Production Research, 54(8), 2203-2214. [DOI:10.1080/00207543.2015.1051668]
Ramirez-Nafarrate, A., Araz, O. M., & Fowler, J. W. (2021). Decision assessment algorithms for location and capacity optimization under resource shortages. Decision Sciences, 52(1), 142-181. [DOI:10.1111/deci.12418]
Apte, A., Heidtke, C., & Salmerón, J. (2015). Casualty collection points optimization: A study for the district of Columbia. Interfaces, 45(2), 149-165. [DOI:10.1287/inte.2014.0757]
Lee, E. K., Smalley, H. K., Zhang, Y., Pietz, F., & Benecke, B. (2009). Facility location and multi-modality mass dispensing strategies and emergency response for biodefence and infectious disease outbreaks. International Journal of Risk Assessment and Management, 12(2-4), 311-351. [DOI:10.1504/IJRAM.2009.025925]
Zaric, G. S., & Brandeau, M. L. (2002). Dynamic resource allocation for epidemic control in multiple populations. Mathematical Medicine and Biology: A Journal of the IMA, 19(4), 235-255. [DOI:10.1093/imammb/19.4.235]
Ouyang, H., Argon, N. T., & Ziya, S. (2020). Allocation of intensive care unit beds in periods of high demand. Operations Research, 68(2), 591-608. [DOI:10.1287/opre.2019.1876]
Koyuncu, M., & Erol, R. (2010). Optimal resource allocation model to mitigate the impact of pandemic influenza: A case study for Turkey. Journal of Medical Systems, 34(1), 61-70. [DOI:10.1007/s10916-008-9216-y] [PMID]
Apornak, A. (2021). Human resources allocation in the hospital emergency department during COVID-19 pandemic. International Journal of Healthcare Management, 14(1), 264-270. [DOI:10.1080/20479700.2020.1861173]
Anparasan, A., & Lejeune, M. (2019). Resource deployment and donation allocation for epidemic outbreaks. Annals of Operations Research, 283, 9-32. [DOI:10.1007/s10479-016-2392-0]
Ross, T. J. (2009). Fuzzy logic with engineering applications. New Jerse: John Wiley & Sons. [Link]
Worldometer. (2023). COVID-19 Coronavirus Pandemic. Retrieved from: [Link]
ED-DAT. (2023). EM-DAT public. Retrieved from: [Link]