特大暴雨下城市地铁人员应急救援与疏散优化

Optimization of emergency rescue and evacuation for urban subway personnel in severe rainstorms

为提高特大暴雨下地铁车站人员救援与疏散效率,提出地铁人员的应急救援与疏散优化建模与求解方法,旨在协同优化救援中心选址、应急路径设计与分配决策。针对特大暴雨的危害性和地铁被困人员的心理恐慌程度,改进感知风险度量模型。结合应急救援与疏散的阶段性,建立成本和感知风险最小的应急救援与疏散的选址-路径优化模型。设计基于分解多目标进化算法和分支切割算法的两阶段求解步骤。最后,通过郑州实例和测试算例,验证新模型和算法的有效性。计算结果表明:新模型和算法能在2.13 s内求得有效方案;相较于传统风险模型,新模型能够降低9.03%的运输成本;相较于常规的多目标优化算法,新算法能缩短至少60.00%的求解时间,并有较高的计算稳定性。

In order to improve the efficiency of rescue and evacuation of the people trapped in the subway station in severe rainstorms,an optimization model and solution approach for subway personnel's emergency rescue and evacuation is developed,which decides the facility location,emergency route and flow allocation simultaneously.Considering the rainstorms’severity and the psychological panic of trapped people during emergency rescue,an improved perceived risk assessment is designed.Then,addressing the framework of multi-stage emergency rescue and evacuation system,a location-routing model,minimizing the total cost and perceived risk,is developed for the subways emergency rescue and evacuation.The two-stage solution approach based on decomposition techniques using multi-objective evolutionary and branch and cut algorithms is designed.Finally,a case study of Zhengzhou and several tests are provided to evaluate the effectiveness.The computational results show that,new model and algorithm can provide an efficient plan within 2.13 seconds,and has certain sensitivity to key parameters.Comparing to the traditional model,the new risk assessment can reduce the transportation cost by 9.03%.Comparing to the general multi-objective optimization methods,the new algorithm can reduce the calculation time by at least 60.00%,and keeps stable performance in solving problems of different scales.