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

Wilson, Duncan, Hawe, Glenn, Coates, Graham and Crouch, Roger (2016) Online optimization of casualty processing in major incident response: An experimental analysis. European Journal of Operational Research, 252 (1). pp. 334-348. [Journal article]

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DOI: doi:10.1016/j.ejor.2016.01.021


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.

Item Type:Journal article
Keywords:Scheduling; Combinatorial optimization; Emergency response; Disaster management; Dynamic
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Mathematics
Research Institutes and Groups:Computer Science Research Institute
Computer Science Research Institute > Artificial Intelligence and Applications
ID Code:33430
Deposited By: Dr Glenn Hawe
Deposited On:08 Mar 2016 09:29
Last Modified:11 Jul 2017 10:04

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