The Bin Packing Problem (BPP) is a well-established combinatorial optimization (CO) problem. Since it has many applications in our daily life, e.g. logistics and resource allocation, people are seeking efficient bin packing algorithms. On the other hand, researchers have been making constant advances in machine learning (ML), which is famous for its efficiency. In this article, we first formulate BPP, introducing its variants and practical constraints. Then, a comprehensive survey on ML for multi-dimensional BPP is provided. We further collect some public benchmarks of 3D BPP, and evaluate some online methods on the Cutting Stock Dataset. Finally, we share our perspective on challenges and future directions in BPP. To the best of our knowledge, this is the first systematic review of ML-related methods for BPP.

翻译：装箱问题（BPP）是一类经典的组合优化问题。由于其在物流和资源分配等日常场景中具有广泛应用，人们始终在寻求高效的装箱算法。与此同时，以高效性著称的机器学习领域正不断取得突破性进展。本文首先对BPP进行系统建模，介绍其变体形式及实际约束条件；随后全面综述了机器学习在多维BPP中的应用现状。我们进一步整理了三维BPP的公开基准数据集，并在切割原料数据集上评估了若干在线方法。最后，就BPP面临的挑战与未来研究方向提出见解。据我们所知，这是首篇系统梳理BPP机器学习方法的综述性论文。