This paper aims to solve the large-scale heterogeneous container loading problem (HCLP), which is an extensive form of the multiple container loading problem, in a limited time. The target is to choose a set of containers of different sizes to accommodate all products and minimize the wasted space rate. Although the heterogeneous container selection problem is a general problem in the logistics industry, few related studies have been conducted. This study also considers some practical constraints, such as weight limits and suspension constraints. A hybrid adaptive large neighborhood search (HALNS) algorithm, which includes a set of original destroy-repair operators, especially for heterogeneous container selection problems, and integrates a heuristic packing algorithm, is proposed to solve the problem in an acceptable time. To verify the efficiency of the proposed algorithm, computational experiments on real-world instances from a multinational logistics company are performed, and the results are compared with those of other existing algorithms. The results indicate that the proposed algorithm outperforms other algorithms for the HCLP.

本文旨在在有限的时间内解决大规模异构集装箱装载问题（HCLP），这是多集装箱装载问题的扩展形式。目标是选择一组不同尺寸的容器来容纳所有产品，并最大限度地减少空间浪费率。虽然异构容器选择问题是物流行业普遍存在的问题，但相关研究很少。本研究还考虑了一些实际约束，例如重量限制和悬架约束。提出了一种混合自适应大邻域搜索（HALNS）算法，该算法包括一组原始的破坏修复算子，特别是针对异构容器选择问题，并集成了启发式打包算法，以在可接受的时间内解决该问题。为了验证所提出算法的效率，对来自跨国物流公司的真实世界实例进行了计算实验，并将结果与​​其他现有算法的结果进行了比较。结果表明，对于HCLP，所提出的算法优于其他算法。