三维装箱难题，OPAL 不只会选位置，还用操作引导生成候选加学习排序，BED-BPP 空间利用率 0.49，候选生成贡献 15.1%。
论文提出 OPAL，一个针对工业在线三维装箱问题的操作引导放置感知学习框架。OPAL 将 OG-EMS 候选生成器、基于 xLSTM 的放置编码器与 PPO 训练的掩码排序策略相结合，对候选位置进行综合排序。在 BED-BPP 基准上，OPAL 的平均空间利用率达到 0.49，其中操作引导候选生成贡献 15.1% 的提升，学习排序贡献 6.3%。该方法同时关注空间利用率、稳定性、紧凑性和平衡性，面向实际工业码垛场景。
Operationally Guided Placement-Aware Learning for Industrial Online 3D Bin Packing
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.