Operationally Guided Placement-Aware Learning for Industrial Online 3D Bin Packing

2026-07-30

AI Summary

This work addresses the low space utilization and poor stability in industrial online 3D bin packing, which stem from a lack of operability-aware guidance in candidate placement generation. To this end, we propose OPAL, a novel framework that integrates operability constraints directly into candidate generation and representation. OPAL employs an Operability-Guided Empty Maximal Space (OG-EMS) generator to enhance supportiveness, compactness, and spatial diversity of placements, introduces an operability-oriented candidate representation, and leverages xLSTM to model dependencies between geometric and operational attributes. A lightweight recurrent core combined with a mask-based ranking strategy enables efficient Proximal Policy Optimization (PPO) training. Evaluated on the BED-BPP benchmark, OPAL achieves an average space utilization of 0.49, with operability-guided generation contributing a 15.1% improvement and learned ranking adding a further 6.3%, all while maintaining high inference efficiency.

Abstract

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.

Problem

• online 3D bin packing
• candidate placement generation
• placement representation
• industrial palletizing
• space utilization

Innovation

• operationally guided placement
• online 3D bin packing
• placement-aware learning
• OG-EMS
• xLSTM-based encoder