The container relocation problem is a combinatorial optimisation problem aimed at finding a sequence of container relocations to retrieve all containers in a predetermined order by minimising a given objective. Relocation rules (RRs), which consist of a priority function and relocation scheme, are heuristics commonly used for solving the mentioned problem due to their flexibility and efficiency. Recently, in many real-world problems it is becoming increasingly important to consider energy consumption. However, for this variant no RRs exist and would need to be designed manually. One possibility to circumvent this issue is by applying hyperheuristics to automatically design new RRs. In this study we use genetic programming to obtain priority functions used in RRs whose goal is to minimise energy consumption. We compare the proposed approach with a genetic algorithm from the literature used to design the priority function. The results obtained demonstrate that the RRs designed by genetic programming achieve the best performance.

翻译：集装箱重定位问题是一个组合优化问题，旨在通过最小化给定目标，找到一系列集装箱重定位操作，以按预定顺序取出所有集装箱。搬迁规则由优先级函数和搬迁方案组成，因其灵活性和高效性而成为解决该问题的常用启发式方法。近年来，在许多实际场景中，考虑能耗变得越来越重要。然而，针对这一变体问题，目前尚无现成的搬迁规则，需要人工设计。规避这一问题的一种方法是应用超启发式算法来自动设计新的搬迁规则。本研究采用遗传编程来自动获取搬迁规则中旨在最小化能耗的优先级函数。我们将所提出的方法与文献中用于设计优先级函数的遗传算法进行了比较。结果表明，由遗传编程设计的搬迁规则取得了最佳性能。