Train timetable rescheduling (TTR) aims to promptly restore the original operation of trains after unexpected disturbances or disruptions. Currently, this work is still done manually by train dispatchers, which is challenging to maintain performance under various problem instances. To mitigate this issue, this study proposes a reinforcement learning-based approach to TTR, which makes the following contributions compared to existing work. First, we design a simple directed graph to represent the TTR problem, enabling the automatic extraction of informative states through graph neural networks. Second, we reformulate the construction process of TTR's solution, not only decoupling the decision model from the problem size but also ensuring the generated scheme's feasibility. Third, we design a learning curriculum for our model to handle the scenarios with different levels of delay. Finally, a simple local search method is proposed to assist the learned decision model, which can significantly improve solution quality with little additional computation cost, further enhancing the practical value of our method. Extensive experimental results demonstrate the effectiveness of our method. The learned decision model can achieve better performance for various problems with varying degrees of train delay and different scales when compared to handcrafted rules and state-of-the-art solvers.

火车时刻表重新调度的研究中，通过基于强化学习的方法，设计了一个简单的有向图来表示问题，并利用图神经网络自动提取信息状态；通过改变解决方案的构建过程，使得决策模型与问题规模解耦合，确保生成方案的可行性；为模型设计了一个学习课程来处理不同程度延误的情况；并且提出了一个简单的局部搜索方法，显著提高了解决方案的质量，实验结果证明了该方法在解决各种不同程度火车延误和规模问题时比手工规则和现有解决方法具有更好的性能。

基于图表示的可扩展列车时刻表重排的强化学习