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.
翻译:集装箱搬迁问题是一个具有挑战性的组合优化问题,任务是寻找按特定顺序取回所有集装箱所需的集装箱搬迁顺序。由于这一问题的复杂性,通常会采用累进式方法在小段时间内获得可接受的解决办法,其中包括确定根据某些院落特性有效取回下一个集装箱而需要进行的搬迁规则(RRs),这些规则通常由域专家手工设计,这是一项耗时和具有挑战性的任务。本文件调查基因编程应用情况,以自动设计有效的RR(GP)。实验结果表明,GP发展出RRs超越了现有的几个手动设计的RRs。对拟议方法的进一步分析表明,演变的规则广泛概括了广泛的无形问题,其性能可以进一步提高。因此,拟议方法为现有手工设计的RRs提供了一种可行的替代方法,为集装箱搬迁问题领域开辟了新的研究方向。