Reinforcement Learning for Scalable Train Timetable Rescheduling with
  Graph Representation
Peng Yue Yaochu Jin Xuewu Dai Zhenhua Feng Dongliang Cui
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.