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列车运行调整问题是铁路行车调度指挥工作的重要内容,决定着区段内行车秩序的优劣。这一问题的计算机自动求解算法是我国铁路信息化建设的一个核心技术和难点问题。本文依据我国铁路行车组织体制的特点,建立了相应的模型。在模型的求解过程中,先运用大系统理论将列车进行分层分级,从而将待解的原始问题分解成若干个子问题,在对分解后的问题进行求解时,设计了微粒群算法,运用该算法可快速得到各子问题的近似最优解。然后,应用系统原理对问题进行还原,即可快速得到一个满意度高、可用性强的列车运行调整方案。最后,采用现场数据,应用该算法对列车运行调整问题进行求解,并与遗传算法进行比较,结果表明微粒群算法解决列车运行调整问题高效、实用。
The adjustment of train operation is an important part of command and control of railway traffic dispatching and determines the pros and cons of driving order in the section. The automatic computer algorithm for solving this problem is a core technology and difficult issue in China’s railway information construction. Based on the characteristics of China’s railway driving organization system, this paper establishes the corresponding model. In the process of solving the model, the large-scale system theory is used to classify the train hierarchically, so that the original problem to be solved is decomposed into several sub-problems. When solving the problem, the particle swarm optimization algorithm is designed. The algorithm can quickly obtain the approximate optimal solution of each sub-problem. Then, applying the principle of system to restore the problem, we can quickly get a plan of train operation adjustment with high satisfaction and availability. Finally, using the field data, the algorithm is applied to solve the train operation adjustment problem and compared with the genetic algorithm. The results show that the PSO can solve the problem of train operation adjustment efficiently and practically.