The container relocation problem is a challenging combinatorial optimisation problem tasked with finding a sequence of container relocations required to retrieve all containers by a given order. Due to the complexity of this problem, heuristic methods are often applied to obtain acceptable solutions in a small amount of time. These include relocation rules (RRs) that determine the relocation moves that need to be performed to efficiently retrieve the next container based on certain yard properties. Such rules are often designed manually by domain experts, which is a time-consuming and challenging task. This paper investigates the application of genetic programming (GP) to design effective RRs automatically. The experimental results show that GP evolved RRs outperform several existing manually designed RRs. Additional analyses of the proposed approach demonstrate that the evolved rules generalise well across a wide range of unseen problems and that their performance can be further enhanced. Therefore, the proposed method presents a viable alternative to existing manually designed RRs and opens a new research direction in the area of container relocation problems.

中文翻译：

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集装箱搬迁问题启发式的自动化设计

容器重定位问题是一个具有挑战性的组合优化问题，其任务是找到按给定顺序检索所有容器所需的一系列容器重定位。由于这个问题的复杂性，通常采用启发式方法来在短时间内获得可接受的解决方案。其中包括重新定位规则 (RR)，这些规则确定需要执行的重新定位移动，以根据某些堆场属性有效地取回下一个集装箱。此类规则通常由领域专家手动设计，这是一项耗时且具有挑战性的任务。本文研究了遗传编程 (GP) 在自动设计有效 RR 中的应用。实验结果表明，GP 进化的 RRs 优于几个现有的手动设计的 RRs。对所提出方法的其他分析表明，演化规则可以很好地泛化广泛的未知问题，并且可以进一步提高其性能。因此，所提出的方法为现有手动设计的 RR 提供了一种可行的替代方案，并在集装箱搬迁问题领域开辟了新的研究方向。