The container relocation problem is one of important issues in seaport terminals which could bring a significant saving on the operating cost even with a slight improvement due to the huge number of containers processed across the world each year. Given a specific layout and container retrieval priorities, the container relocation problem aims to find the optimal movement sequence to minimize the total number of container relocation operations. In this paper, we propose novel machine learning-driven algorithms, which integrate optimization methods and machine learning techniques, to solve the problem. More specifically, we propose a new upper bound method called MLUB that incorporates branch pruners. These pruners are derived from some machine learning techniques through using the optimal solution values of many small-scale instances. The tightened upper bounds generated by MLUB are used subsequently both in the exact branch-and-bound algorithm called IB&B and the hybrid beam search heuristic called MLBS. Moreover, we also provide a tighter lower bound for the problem by additionally considering the interaction between consecutive target containers. Based on the benchmark data published recently in the literature, extensive experiments are conducted to test the performance of the proposed algorithms. The experimental results demonstrate that the proposed algorithms outperform the state-of-the-art algorithms reported in the literature, and some managerial insights regarding the load intensity of the bay and some algorithm parameters such as the look-ahead depth and the beam width are drawn from the results.

集装箱搬迁问题是海港码头中的重要问题之一，由于每年全球处理的集装箱数量巨大，即使略有改善，也可以大大节省运营成本。给定特定的布局和容器取回优先级，容器重定位问题旨在找到最佳移动顺序，以最大程度地减少容器重定位操作的总数。在本文中，我们提出了新颖的机器学习驱动算法，该算法将优化方法和机器学习技术相结合，以解决该问题。更具体地说，我们提出了一种新的上限方法，称为MLUB，它结合了分支修剪器。这些修剪器是通过使用许多小规模实例的最佳解决方案值从某些机器学习技术衍生而来的。由MLUB生成的更严格的上限随后被用于称为IB＆B的精确分支定界算法和称为MLBS的混合波束搜索启发式算法。此外，通过另外考虑连续目标容器之间的交互作用，我们还为问题提供了更严格的下限。基于最近在文献中发布的基准数据，进行了广泛的实验以测试所提出算法的性能。实验结果表明，所提出的算法优于文献报道的最新算法，并且在管理上对舱室的负载强度以及一些算法参数（如前瞻深度和波束宽度）的见解也得到了改善。从结果中得出。