Online optimization of casualty processing in major incident response: An experimental analysis
针对大规模伤亡事件响应中的动态和不确定性问题，扩展了一个多目标组合优化模型，使其能在实时环境中运行，并通过仿真实验评估了通信速度和任务时长估计误差对模型效用的影响。
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.