Abstract
During the emergency response to multiple mass casualty incidents (MCIs), a number of coordination (allocation) decisions need to be made in a timely manner. This paper reports on an optimization-based approach that has been developed to solve the ambulance-to-casualty and casualty-to-hospital allocation problems. A number of constraints are taken into consideration such as the number of ambulances and hospitals, along with the capacity of hospitals. Within the approach, the road network of the geographical area under consideration is modelled realistically. Further, the day of the week and the time of day at which multiple MCIs occur are considered as factors influencing the speed of the ambulances. The approach includes a Neighborhood Search Algorithm that has been developed and used to obtain solutions to a multiple MCI case study involving a number of scenarios.
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References
The UK Cabinet Office: National Risk Register of Civil Emergencies (2017)
Chen, R., Sharman, R., Rao, H.R., Upadhyaya, S.J.: Coordination in emergency response management. Commun. ACM. 51(5), 66–73 (2008)
Wilson, D.T., Hawe, G.I., Coates, G., Crouch, R.S.: Online optimization of casualty processing in major incident response: an experimental analysis. Eur. J. Oper. Res. 252(1), 334–348 (2016)
Repoussis, P.P., Paraskevopoulos, D.C., Vazacopoulos, A., Hupert, N.: Optimizing emergency preparedness and resource utilization in mass-casualty incidents. Eur. J. Oper. Res. 255, 531–544 (2016)
Amram, O., Schuurman, N., Hedley, N.: A web-based model to support patient-to-hospital allocation in mass casualty incidents. J. Trauma. 72(5), 1323–1328 (2012)
Salman, F.S., Gul, S.: Deployment of field hospitals in mass ¨ casualty incidents. Comput. Ind. Eng. 74(1), 37–51 (2014)
Hupert, N., Hollingsworth, E., Xiong, W.: Overtriage and outcomes: insights from a computer model of trauma system mass casualty response. Disaster Med. Public Health Prep. 1(Suppl. 1), 14–24 (2007)
Sung, I., Lee, T.: Optimal allocation of emergency medical resources in a mass casualty incident: patient prioritization by column generation. Eur. J. Oper. Res. 252(2), 623–634 (2016)
Mills, A.F., Argon, N.T., Ziya, S.: Resource-based patient prioritization in mass-casualty incidents. Manuf. Serv. Oper. Manag. 15(3), 361–377 (2013)
Gong, Q., Batta, R.: Allocation and reallocation of ambulances to casualty clusters in a disaster relief operation. IIE Trans. 39(1), 27–39 (2007)
Mete, H.O., Zabinsky, Z.B.: Stochastic optimization of medical supply location and distribution in disaster management. Int. J. Prod. Econ. 126(1), 76–84 (2010)
McCormack, R., Coates, G.: A simulation model to enable the optimization of ambulance fleet allocation and base station location for increased patient survival. Eur. J. Oper. Res. 247(1), 294–309 (2015)
Niessner, H., Rauner, M.S., Gutjahr, W.J.: A dynamic simulation–optimization approach for managing mass casualty incidents. Oper. Res. Health Care. 17, 82–100 (2017)
Kolesar, P., Walker, W., Hausner, J.: Determining the relation between fire engine travel times and travel distances in New York City. Oper. Res. 23(4), 614–627 (1975)
Ordnance Survey. Ordnance survey: Britain’s mapping agency. (Online). https://www.ordnancesurvey.co.uk/. Accessed 07 Feb 2019
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Aldossary, H., Coates, G. (2019). Coordinating the Emergency Response of Ambulances to Multiple Mass Casualty Incidents using an Optimization-based Approach. In: Paolucci, M., Sciomachen, A., Uberti, P. (eds) Advances in Optimization and Decision Science for Society, Services and Enterprises. AIRO Springer Series, vol 3. Springer, Cham. https://doi.org/10.1007/978-3-030-34960-8_15
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DOI: https://doi.org/10.1007/978-3-030-34960-8_15
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