One4Many-StablePacker: An Efficient Deep Reinforcement Learning Framework for the 3D Bin Packing Problem

• URL: http://arxiv.org/abs/2510.10057v1
• Date: Sat, 11 Oct 2025 06:47:49 GMT
• Title: One4Many-StablePacker: An Efficient Deep Reinforcement Learning Framework for the 3D Bin Packing Problem
• Authors: Lei Gao, Shihong Huang, Shengjie Wang, Hong Ma, Feng Zhang, Hengda Bao, Qichang Chen, Weihua Zhou,
• Abstract summary: Three-dimensional bin packing problem (3D-BPP) is widely applied in logistics and warehousing.<n>We propose a novel deep reinforcement learning framework, One4Many-StablePacker (O4M-SP)<n>O4M-SP is able to handle various bin dimensions in a single training process while incorporating support and weight constraints common in practice.
• Score: 12.516955835907089
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The three-dimensional bin packing problem (3D-BPP) is widely applied in logistics and warehousing. Existing learning-based approaches often neglect practical stability-related constraints and exhibit limitations in generalizing across diverse bin dimensions. To address these limitations, we propose a novel deep reinforcement learning framework, One4Many-StablePacker (O4M-SP). The primary advantage of O4M-SP is its ability to handle various bin dimensions in a single training process while incorporating support and weight constraints common in practice. Our training method introduces two innovative mechanisms. First, it employs a weighted reward function that integrates loading rate and a new height difference metric for packing layouts, promoting improved bin utilization through flatter packing configurations. Second, it combines clipped policy gradient optimization with a tailored policy drifting method to mitigate policy entropy collapse, encouraging exploration at critical decision nodes during packing to avoid suboptimal solutions. Extensive experiments demonstrate that O4M-SP generalizes successfully across diverse bin dimensions and significantly outperforms baseline methods. Furthermore, O4M-SP exhibits strong practical applicability by effectively addressing packing scenarios with stability constraints.

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