基于图表示的强化学习用于可扩展列车时刻表重调度
Reinforcement Learning for Scalable Train Timetable Rescheduling with Graph Representation
AI总结：
本文提出一种基于强化学习和图神经网络的列车时刻表重调度方法，通过有向图表示、解耦决策与问题规模、课程学习及局部搜索，在多种延误和规模场景下优于手工规则和现有求解器。
摘要：
列车时刻表重调度（TTR）旨在在突发扰动或中断后迅速恢复列车的正常运行。目前，这项工作仍由列车调度员人工完成，难以在各种问题实例下保持性能。为缓解这一问题，本研究提出了一种基于强化学习的TTR方法，与现有工作相比，具有以下贡献。首先，我们设计了一个简单的有向图来表示TTR问题，从而能够通过图神经网络自动提取有信息量的状态。其次，我们重新构造了TTR解的构建过程，不仅使决策模型与问题规模解耦，而且保证了生成方案的可行性。第三，我们为模型设计了一个学习课程，以处理不同程度延误的场景。最后，提出了一种简单的局部搜索方法来辅助学习到的决策模型，该方法能以很少的额外计算成本显著提高解的质量，进一步增强我们方法的实用价值。大量实验结果表明了我们方法的有效性。与手工规则和最先进的求解器相比，学习到的决策模型在具有不同程度列车延误和不同规模的各种问题上都能取得更好的性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
