针对目前轨道检测车作业路径缺乏系统规划方法的弊端，以城市轨道交通线网为背景，构建成网条件下多约束地铁大型检测车辆路径优化模型(urban track inspection vehicle routing problem，UTIVRP)。针对地铁线网的特点，设计具有特殊编码方式的文化基因算法，并通过北京地铁实际算例予以验证。计算结果表明，在满足既定检测要求的情况下，优化方案不仅能够减少车辆 48.88%的空走里程，而且能够将线网的检测间隔最大偏差率降低93.33%。

The subway engineering department regularly operates track inspection vehicles to detect the state of the tracks,which is crucial for residents’ safe travel. The operational path of track inspection vehicles mainly relies on expert judgment,which is not only a time-consuming practice but is also ineffective. To address the shortcomings of the current lack of systematicplanning for the operational paths of track inspection vehicles, this study, set against the backdrop of the urban rail transitnetwork, constructs a large-scale subway inspection vehicle routing optimization model named Urban Track Inspection VehicleRouting Problem (UTIVRP), under the conditions of a complex network. Considering the characteristics of subway networks,a cultural genetic algorithm with a special encoding method is designed and validated using practical examples from theBeijing subway. The computational results indicate that under the conditions of meeting the established inspection requirements,the optimization solution can not only reduce the idle mileage of vehicles by 48.88%, but also decrease the maximum deviationrate of the network’s inspection interval by 93.33%.

U231

中国国家铁路集团有限公司科技研究开发计划(N2022G028)