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

Duncan Wilson, Glenn Hawe, Graham Coates, Roger Crouch

Research output: Contribution to journalArticlepeer-review

19 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)334-348
Number of pages15
JournalEuropean Journal of Operational Research
Issue number1
Early online date22 Jan 2016
Publication statusPublished (in print/issue) - 1 Jul 2016


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


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