  Parking, matching, scheduling, and routing are common problems in train
maintenance. In particular, train units are commonly maintained and cleaned at
dedicated shunting yards. The planning problem that results from such
situations is referred to as the Train Unit Shunting Problem (TUSP). This
problem involves matching arriving train units to service tasks and determining
the schedule for departing trains. The TUSP is an important problem as it is
used to determine the capacity of shunting yards and arises as a sub-problem of
more general scheduling and planning problems. In this paper, we consider the
case of the Dutch Railways (NS) TUSP. As the TUSP is complex, NS currently uses
a local search (LS) heuristic to determine if an instance of the TUSP has a
feasible solution. Given the number of shunting yards and the size of the
planning problems, improving the evaluation speed of the LS brings significant
computational gain. In this work, we use a machine learning approach that
complements the LS and accelerates the search process. We use a Deep Graph
Convolutional Neural Network (DGCNN) model to predict the feasibility of
solutions obtained during the run of the LS heuristic. We use this model to
decide whether to continue or abort the search process. In this way, the
computation time is used more efficiently as it is spent on instances that are
more likely to be feasible. Using simulations based on real-life instances of
the TUSP, we show how our approach improves upon the previous method on
prediction accuracy and leads to computational gains for the decision-making
process.
