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

Duncan Wilson, Glenn Hawe, Graham Coates, Roger Crouch

Research output: Contribution to journal › Article

5 Citations (Scopus)

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.
LanguageEnglish
Pages334-348
JournalEuropean Journal of Operational Research
Volume252
Issue number1
DOIs
Publication statusPublished - 1 Jul 2016

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Processing
Online systems
Communication
Combinatorial optimization
Experiments

Keywords

  • Scheduling
  • Combinatorial optimization
  • Emergency response
  • Disaster management
  • Dynamic

Cite this

Wilson, Duncan ; Hawe, Glenn ; Coates, Graham ; Crouch, Roger. / Online optimization of casualty processing in major incident response: An experimental analysis. 2016 ; Vol. 252, No. 1. pp. 334-348.
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Wilson, D, Hawe, G, Coates, G & Crouch, R 2016, 'Online optimization of casualty processing in major incident response: An experimental analysis', vol. 252, no. 1, pp. 334-348. https://doi.org/10.1016/j.ejor.2016.01.021

Online optimization of casualty processing in major incident response: An experimental analysis. / Wilson, Duncan; Hawe, Glenn; Coates, Graham; Crouch, Roger.

Vol. 252, No. 1, 01.07.2016, p. 334-348.

Research output: Contribution to journal › Article

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AB - 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.

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KW - Disaster management

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

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