AI 中文总结
针对工业在线三维装箱问题，提出OPAL框架，结合操作引导式候选生成等技术，在BED-BPP基准上提升空间利用率，保持稳健推理性能。
AI 中文摘要
在线三维装箱问题（3D-BPP）是物流和工业码垛领域长期存在的挑战。近期基于学习的方法会利用学习到的策略从可行的候选放置方案中进行选择，其性能取决于候选生成器和表示方式，尤其是在工业场景中，装箱必须满足空间利用率高、稳定、紧凑且平衡的要求。然而，现有研究主要聚焦于策略优化，而候选生成和表示方式仍主要基于几何驱动。本文针对这一差距提出了OPAL，这是一个面向工业在线三维装箱问题的操作引导式放置感知学习框架，它结合了操作引导式空最大空间生成器（OG-EMS）、每个候选放置方案的操作表示，以及通过近端策略优化训练的掩码排序策略。OG-EMS会评估每个自由空间区域内的多个锚点，并优先选择低矮、支撑良好、紧凑且空间分布多样的放置方案。基于xLSTM的放置编码器会对几何和操作类候选属性之间的依赖关系进行建模，而轻量级循环核心会将生成的嵌入与当前物品和托盘状态相结合，以对可行动作进行排序。在BED-BPP基准测试中，OPAL实现了0.49的平均空间利用率，其中操作引导式候选生成带来了15.1%的提升，学习排序带来了6.3%的提升，同时保持了稳健的推理时间性能。
英文摘要
The online three-dimensional bin packing problem (3D-BPP) is a longstanding challenge in logistics and industrial palletizing. Recent learning-based methods use a learned policy to select among feasible candidate placements. Performance depends on the candidate generator and representation, especially in industrial settings where packings must be space-efficient, stable, compact, and balanced. However, prior work has mainly optimized the policy, while candidate generation and representation remain largely geometry-driven. We address this gap with OPAL, an operationally guided placement-aware learning framework for industrial online 3D-BPP which combines an Operationally Guided Empty-Maximal-Space generator (OG-EMS), an operational representation for each candidate placement, and a masked ranking policy trained with proximal policy optimization. OG-EMS evaluates multiple anchors within each free-space region and prioritizes low, well-supported, compact, and spatially diverse placements. An xLSTM-based Placement Encoder models dependencies among geometric and operational candidate attributes, while a lightweight recurrent core combines the resulting embeddings with the current item and pallet state to rank feasible actions. On the BED-BPP benchmark, OPAL achieves a mean space utilization of 0.49, with improvements of 15.1% from operationally guided candidate generation and 6.3% from learned ranking, while maintaining robust inference-time performance.
