Abstract: Analyzing interactions among various redundant time, a mathematical model is established for redundancy optimization layout. The optimization targets are to minimize traveling time, departure delay time of train and delay time at stop. The algorithm based on genetic algorithm and particle swarm optimization is applied to solve the model by the software MATLAB. Simulation analysis is made with the background of virtual operation schedules by the reasonably designed statistic indexes and simulation schemes of train delays. The results show that the model and algorithm to get the layout of the layout scheme compared with layout scheme based on the fixed ratio, the delay time is short, the number of train delays in the interval and the station are substantially short, compared with layout scheme based on genetic algorithm, the total delay time is short, always redundant set more time, but he number of train delays in the interval and the station are less than layout scheme based on genetic algorithm’s.

Key words: high-speed railway train, redundant time, optimization model, genetic algorithm, particle swarm optimization

摘要： 在分析各种冗余时间之间作用机理的基础上，以列车旅行时间和列车到发站延误时间最短为优化目标，建立运行图冗余时间优化布局模型。在此基础上引入遗传粒子群优化算法对模型进行求解，并用MATLAB仿真。以虚拟运行时刻表为背景，通过合理设置列车运行干扰时间和仿真分析方案，对结果进行分析。结果表明：用该模型和算法得到的布局方案相比较于固定比例方案，总延误时间短，列车在区间和车站的晚点次数少;相比较于遗传算法求解该模型的总延误时间短，总冗余时间设置多，但是列车在车站和区间的晚点次数少。

关键词: 高铁列车, 冗余时间, 优化模型, 遗传算法, 粒子群算法