Title: Deep Learning Assisted Heuristic Tree Search for the Container Pre-marshalling Problem,
Abstract: One of the key challenges for operations researchers solving real-world
problems is designing and implementing high-quality heuristics to guide their
search procedures. In the past, machine learning techniques have failed to play
a major role in operations research approaches, especially in terms of guiding
branching and pruning decisions. We integrate deep neural networks into a
heuristic tree search procedure to decide which branch to choose next and to
estimate a bound for pruning the search tree of an optimization problem. We
call our approach Deep Learning assisted heuristic Tree Search (DLTS) and apply
it to a well-known problem from the container terminals literature, the
container pre-marshalling problem (CPMP). Our approach is able to learn
heuristics customized to the CPMP solely through analyzing the solutions to
CPMP instances, and applies this knowledge within a heuristic tree search to
produce the highest quality heuristic solutions to the CPMP to date.