Deep learning assisted heuristic tree search for the container pre-marshalling problem. (January 2020)

• Record Type: Journal Article
• Authors: Hottung, André; Tanaka, Shunji; Tierney, Kevin
• Is Part Of: Computers & operations research. Volume 113(2020)
• Publication Date: 2020-01
• Subjects: Container pre-marshalling -- Tree search -- Deep learning; Operations research -- Periodicals; Electronic digital computers -- Periodicals; 004.05
• DOI: 10.1016/j.cor.2019.104781
• Languages: English
• ISSNs: 0305-0548

Abstract

Highlights: A tree search algorithm for the container pre-marshalling problem (CPMP) using deep neural networks. Learns solution strategies and lower bounds from existing solutions for the CPMP. Outperforms state-of-the-art methods on real-world sized instances. Provides an evaluation of different search strategies and model generalization. Abstract: 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.