Dynamic Allocation Strategies for Medical Teams in the First Hours after Mass Casualty Incidents

Document Type : Research Paper

Authors

Department of Industrial Engineering, Faculty of Engineering, Yazd University, Yazd, Iran.

Abstract

Allocating a limited number of relief teams to casualties immediately after a disaster is a challenging task embedded in the casualty management process. This paper proposes several dynamic strategies for allocating teams to casualty groups right after a sudden-onset disaster in order to maximize the expected number of survivors. In the proposed strategies, serious triage groups and the deterioration of the physical condition of injured people are considered. The ratio of casualties in red and yellow triage groups, and the treatment rates and survival probabilities are the main parameters in the strategies. Then, a case study is employed to demonstrate the validity of the proposed model. The strategies are compared based on the summation of the ratio of survivors in two triage groups. This comparison shows that the saving rate can be an appropriate ratio for allocating medical teams to casualty groups. Sensitivity analysis evaluates the impact of key parameters on the model results. Accordingly, changes in the ratio of triaged people have less impact on the ratio of survivors than changes in the treatment rates. It demonstrates the importance of relief teams’ allocation for surviving the casualties.

Keywords

References

 
       [1]        Alinaghian, M., Goli, A., & Mokhatab Rafiei, F. (2015). Disaster relief vehicle routing with covering tour approach and fuzzy demands, solved by Hybrid Harmony Search Algorithm. Advances in Industrial Engineering, 49(1), 79-92.
       [2]        Alizadeh, M., Amiri-Aref, M., Mustafee, N., & Matilal, S. (2019). A robust stochastic Casualty Collection Points location problem, European Journal of Operational Research, 279, 3, 965-983.
       [3]        Baghaian, A., Lotfi, M.M. & Rezapour, S. (2022) Integrated deployment of local urban relief teams in the first hours after mass casualty incidents. Operational Research International Journal. https://doi.org/10.1007/s12351-022-00689-y
       [4]        Dean, MD. & Nair, SK. (2014). Mass-casualty triage: Distribution of victims to multiple hospitals using the SAVE model. European Journal of Operational Research 238(1), 363-373.
       [5]        Farahani RZ, Lotfi MM, Baghaian A, Ruiz R, Rezapour S (2020) Mass casualty management in disaster scene: A systematic review of OR&MS research in humanitarian operations. European Journal of Operational Research 287(3), 787–819.
       [6]        Feng, Y., Liu, T., Hu, Z., Wang, D., Cheng, T. C. E., & Yin, Y. (2021). Casualty transport scheduling considering survival probability and injury classification. Computers & Industrial Engineering, 161, 107655.
       [7]        Haeri, A., Reza, M., & Samani, G. (2020). A bi-level programming approach for improving relief logistics operations : A real case in Kermanshah earthquake. Computers & Industrial Engineering, 145(July 2019), 106532.
       [8]        Hodgetts, T., & Macway-Jones, K. (1995). Major incident medical management and support. London: BMJ Publishing Group.
       [9]        Jin, S., Jeong, S., Kim, J., & Kim, K. (2014). A logistics model for the transport of disaster victims with various injuries and survival probabilities. Annals of Operations Research, 230 (1), 17–33.
     [10]      Kamali B, Bish D, & Glick R (2017). Optimal service order for mass-casualty incident response. European Journal of Operational Research 261, 355-367.
     [11]      Lerner, E. B. , Schwartz, R. B. , Coule, P. L. , Weinstein, E. S. , Cone, D. C. , & Hunt, R. C. (2008). Mass casualty triage: An evaluation of the data and devel- opment of a proposed national guideline. Disaster Medicine and Public Health Preparedness, 2 (Supplement 1), S25–S34
     [12]      Li, Y., Zhang, J., & Yu, G. (2020). A scenario-based hybrid robust and stochastic approach for joint planning of relief logistics and casualty distribution considering secondary disasters. Transportation Research Part E: Logistics and Transportation Review, 141(March), 102029.
     [13]      Liu Y, Cui N, & Zhang J (2019). Integrated temporary facility location and casualty allocation planning for post-disaster humanitarian medical service. Transportation Research Part E: Logistics and Transportation Review 128, 1–16.
     [14]      Lodree EJ, Altay N, & Cook RA (2019) Staff assignment policies for a mass casualty event queuing network. Annals of Operations Research 283(1), 411–442.
     [15]      Mills, A. F., Argon, N. T. , & Ziya, S. (2013). Resource-Based patient prioritization in mass-casualty incidents. Manufacturing and Service Operations Management, 15 (3), 361–377 .
     [16]      Oksuz, M.k., Satoglu, S.I., (2020). A two-stage stochastic model for location planning of temporary medical
centers for disaster response. International Journal of Disaster Risk Reduction, 44, 101426.
     [17]      Rezapour, S., Baghaian, A., Naderi, N., & Farzaneh, M. A. (2022). Dynamic on-site treatment strategy for large-scale mass casualty incidents with rescue operation. Computers & Industrial Engineering, 163, 107796.
     [18]      Rezapour S, Naderi N, Morshedlou N, & Rezapourbehnagh S (2018) Optimal deployment of emergency resources in sudden onset disasters. International Journal of Production Economics 204, 365–382.
     [19]      Sabouhi, F., Bozorgi-Amiri, A., & Heydari, M. (2017). A Simultaneous Location, Routing and Scheduling Model for Transporting Evacuees with Time Window and Multiple Depots. Advances in Industrial Engineering, 51(2), 195-206.
     [20]      Sacco, W. J., Navin, D. M. , Fiedler, K. E. , Waddell, R. K. , Long, W. B. , & Buck- man, R. F. (2005). Precise formulation and evidence-based application of re- source-constrained triage. Academic Emergency Medicine, 12 (8), 759–770.
     [21]      Sun H, Wang Y, Zhang J, Cao W (2021a) A robust optimization model for location-transportation problem of disaster casualties with triage and uncertainty. Expert Systems with Applications 175, 114867.
     [22]      Sun H, Wang Y, & Xue Y (2021b) A bi-objective robust optimization model for disaster response planning under uncertainties. Computers and Industrial Engineering 155(99), 107213.
     [23]      Super, G., Groth, S., & Hook, R. (1994). START: Simple triage and rapid treatment plan . Newport Beach, CA: Hoag Memorial Presbyterian Hospital .
     [24]      Tirkolaee, E. B., Aydın, N. S., Ranjbar-Bourani, M., & Weber, G.-W. (2020). A robust bi-objective mathematical model for disaster rescue units allocation and scheduling with learning effect. Computers & Industrial Engineering, 149, 106790.
     [25]      Zafari, F., Shishebori, D. (2019). Designing a Multi-Objective Three-Stage Location-Routing Model for Humanitarian Logistic Planning under Uncertainty. Advances in Industrial Engineering, 53(4), 149-167. 
     [26]      Zhu, L., Gong, Y., Xu, Y., & Gu, J. (2019). Emergency relief routing models for in- jured victims considering equity and priority. Annals of Operations Research, 283 , 1573–1606.
 
Advances in Industrial Engineering
Volume 56, Issue 1
January 2022
Pages 43-55

Files

Share

How to cite

Statistics