1.State Key Laboratory of Synthetical Automation for Process Industries;2.China Railway Shenyang Bureau Group Co., Ltd. Dispatching Office
随着我国高铁建设成网, 列车运行环境更加复杂多变, 对日常行车调度的精细化提出了更高要求. 本文重点研究在大风、雨、雪恶劣天气及设备设施故障等突发事件下造成列车运行晚点时, 在不改变列车运行路径的前提下, 如何通过调整高速列车行车顺序和到发时间, 智能高效地恢复列车按图运行. 本文创新性的将含有到发间隔、越行等多约束的列车运行时间调整问题建模成为三维空间上的最佳路径搜索问题, 提出了一种改进蚁群算法来实现高速列车行车调度优化, 并提出了一个面向高铁调度的信息启发式因子和期望启发式因子的权重组合以及挥发因子的动态调整方法, 用以提高收敛速度和维持解质量. 仿真结果表明, 本文提出的“时间 = 空间”转换模型和权重自适应调整方法能有效提高蚁群搜索求解高铁调度问题的性能, 能实现高速列车行车调度优化.
With the construction of high-speed railway network in China, the train operation environment is more complex and changeable, which puts forward higher requirements for the daily traffic dispatching. This paper focuses on the train running delay caused by sudden events such as strong wind, rain, snow and equipment failure, without changing the train running path, how to intelligently and efficiently restore the high-speed train running according to the diagram by adjusting the train running order and arrival and departure time. This paper innovatively models the problem of train travel time adjustment with multiple constraints, such as arrival and departure interval and overtaking, as an optimal path search problem on the three-dimensional, an improved ant colony algorithm is proposed to realize the optimization of high-speed train dispatching, and a dynamic adjustment method of information heuristic factor and expectation heuristic factor for high-speed train dispatching is proposed, in order to improve the convergence speed and maintain the solution quality. The simulation results show that the “time = space” conversion model and the weight adaptive adjustment method proposed in this paper can effectively improve the performance of ant colony search to solve the high-speed train scheduling problem, and can realize the optimization of high-speed train operation scheduling.