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

arXiv: https://arxiv.org/abs/1709.09972 · HTML: https://mlsanity.com/paper/1709.09972/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Sep 2017.

## Built on by (most cited first, 18 of 18)

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- [Simulation-guided Beam Search for Neural Combinatorial Optimization](https://mlsanity.com/paper/2207.06190/) (2022)
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- [QUBE: Enhancing Automatic Heuristic Design via Quality-Uncertainty Balanced Evolution](https://mlsanity.com/paper/2412.20694/) (2024)
- [Global Optimization: A Machine Learning Approach](https://mlsanity.com/paper/2311.01742/) (2023)
- [UNSAT Solver Synthesis via Monte Carlo Forest Search](https://mlsanity.com/paper/2211.12581/) (2022)
- [Deep learning enhanced mixed integer optimization: Learning to reduce model dimensionality](https://mlsanity.com/paper/2401.09556/) (2024)
- [Algorithmic Prompt-Augmentation for Efficient LLM-Based Heuristic Design for A* Search](https://mlsanity.com/paper/2601.19622/) (2026)
- [Machine learning-based patient selection in an emergency department](https://mlsanity.com/paper/2206.03752/) (2022)
- [Predicting Accurate Lagrangian Multipliers for Mixed Integer Linear Programs](https://mlsanity.com/paper/2310.14659/) (2023)
- [Container pre-marshalling problem minimizing CV@R under uncertainty of ship arrival times](https://mlsanity.com/paper/2405.17576/) (2024)
- [Navigating Demand Uncertainty in Container Shipping: Deep Reinforcement Learning for Enabling Adaptive and Feasible Master Stowage Planning](https://mlsanity.com/paper/2502.12756/) (2025)
- [An Enhanced Iterative Deepening Search Algorithm for the Unrestricted Container Rehandling Problem](https://mlsanity.com/paper/2504.09046/) (2025)
- [Imitation Learning for Combinatorial Optimisation under Uncertainty](https://mlsanity.com/paper/2601.05383/) (2026)

## Abstract (arXiv)

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.