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Online optimization of casualty processing in major incident response: An experimental analysis

Wilson, D.T.; Hawe, G.I.; Coates, G.; Crouch, R.S.

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Authors

D.T. Wilson

G.I. Hawe

G. Coates

R.S. Crouch



Abstract

When designing an optimization model for use in mass casualty incident (MCI) response, the dynamic and uncertain nature of the problem environment poses a significant challenge. Many key problem parameters, such as the number of casualties to be processed, will typically change as the response operation progresses. Other parameters, such as the time required to complete key response tasks, must be estimated and are therefore prone to errors. In this work we extend a multi-objective combinatorial optimization model for MCI response to improve performance in dynamic and uncertain environments. The model is developed to allow for use in real time, with continuous communication between the optimization model and problem environment. A simulation of this problem environment is described, allowing for a series of computational experiments evaluating how model utility is influenced by a range of key dynamic or uncertain problem and model characteristics. It is demonstrated that the move to an online system mitigates against poor communication speed, while errors in the estimation of task duration parameters are shown to significantly reduce model utility.

Citation

Wilson, D., Hawe, G., Coates, G., & Crouch, R. (2016). Online optimization of casualty processing in major incident response: An experimental analysis. European Journal of Operational Research, 252(1), 334-348. https://doi.org/10.1016/j.ejor.2016.01.021

Journal Article Type Article
Acceptance Date Jan 13, 2016
Online Publication Date Jan 22, 2016
Publication Date Jul 1, 2016
Deposit Date Jul 30, 2013
Publicly Available Date Mar 29, 2024
Journal European Journal of Operational Research
Print ISSN 0377-2217
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 252
Issue 1
Pages 334-348
DOI https://doi.org/10.1016/j.ejor.2016.01.021
Keywords Scheduling, Combinatorial optimization, Emergency response, Disaster management, Dynamic.

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