Research Center for Economy of Upper Reaches of the Yangtze River, Chongqing Technology and Business University, Chongqing, China.
Front Public Health. 2023 Feb 20;11:995829. doi: 10.3389/fpubh.2023.995829. eCollection 2023.
Scientifically organizing emergency rescue activities to reduce mortality in the early stage of earthquakes.
A robust casualty scheduling problem to reduce the total expected death probability of the casualties is studied by considering scenarios of disrupted medical points and routes. The problem is described as a 0-1 mixed integer nonlinear programming model. An improved particle swarm optimization (PSO) algorithm is introduced to solve the model. A case study of the Lushan earthquake in China is conducted to verify the feasibility and effectiveness of the model and algorithm.
The results show that the proposed PSO algorithm is superior to the compared genetic algorithm, immune optimization algorithm, and differential evolution algorithm. The optimization results are still robust and reliable even if some medical points fail and routes are disrupted in affected areas when considering point-edge mixed failure scenarios.
Decision makers can balance casualty treatment and system reliability based on the degree of risk preference considering the uncertainty of casualties, to achieve the optimal casualty scheduling effect.
科学组织地震应急救援活动，降低地震早期的死亡率。
考虑到医疗点和路线中断的情况，研究了一个稳健的伤亡人员调度问题，以降低伤亡人员的总预期死亡概率。该问题被描述为一个 0-1 混合整数非线性规划模型。引入了一种改进的粒子群优化（PSO）算法来求解该模型。以中国芦山地震为例，验证了模型和算法的可行性和有效性。
结果表明，所提出的 PSO 算法优于比较的遗传算法、免疫优化算法和差分进化算法。即使在考虑点-边混合失效场景时，受灾地区的一些医疗点失效和路线中断，优化结果仍然是稳健和可靠的。
决策者可以根据风险偏好程度，在考虑伤亡人员不确定性的基础上，权衡伤员救治和系统可靠性，以达到最优的伤员调度效果。
