D.T. Wilson
Online optimization of casualty processing in major incident response: An experimental analysis
Wilson, D.T.; Hawe, G.I.; Coates, G.; Crouch, R.S.
Authors
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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Copyright Statement
Advance online version © 2016 The Authors. Published by Elsevier B.V This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Published Journal Article (Final published version)
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Publisher Licence URL
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Copyright Statement
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