Deep Learning Assisted Heuristic Tree Search for the Container Pre-marshalling Problem
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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.
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