  The container pre-marshalling problem (CPMP) is concerned with the
re-ordering of containers in container terminals during off-peak times so that
containers can be quickly retrieved when the port is busy. The problem has
received significant attention in the literature and is addressed by a large
number of exact and heuristic methods. Existing methods for the CPMP heavily
rely on problem-specific components (e.g., proven lower bounds) that need to be
developed by domain experts with knowledge of optimization techniques and a
deep understanding of the problem at hand. With the goal to automate the costly
and time-intensive design of heuristics for the CPMP, we propose a new method
called Deep Learning Heuristic Tree Search (DLTS). It uses deep neural networks
to learn solution strategies and lower bounds customized to the CPMP solely
through analyzing existing (near-) optimal solutions to CPMP instances. The
networks are then integrated into a tree search procedure to decide which
branch to choose next and to prune the search tree. DLTS produces the highest
quality heuristic solutions to the CPMP to date with gaps to optimality below
2% on real-world sized instances.
