解决3D装箱实时稳定性问题，支持安全重排提升空间利用率。
Online 3D Bin Packing with Fast Stability Validation and Stable Rearrangement Planning
- 引入LBCP快速判断稳定放置位置，避免容器坍塌。
- 提出SRP模块，重排旧物品以容纳新物品，减少搬运成本。
- 在标准数据集上验证了稳定性与通用性，适合工业物流场景。
在线装箱问题（OBPP）是序列决策任务，每个物品需立即放置且无法预知未来来货。尽管近期深度强化学习方法在体积利用率上优于传统启发式算法，但学习到的策略无法保证容器结构稳定性，且缺乏安全重排机制。本文提出融合装箱策略、稳定性验证与启发式重排规划的新框架。创新性地引入负载可承受凸多边形（LBCP），高效识别确保不坍塌的稳定放置位置；并设计稳定重排规划（SRP）模块，在不破坏整体稳定性的前提下重新布局现有物品以容纳新物品。在标准OBPP基准上的大量实验表明，基于LBCP的稳定性验证具有效率与泛化能力，而SRP在寻找省力重排方案上表现更优。本方法为真实工业与物流场景中的自动化装箱提供了鲁棒且实用的解决方案。
原文摘要 · Abstract (English)
The Online Bin Packing Problem (OBPP) is a sequential decision-making task in which each item must be placed immediately upon arrival, with no knowledge of future arrivals. Although recent deep-reinforcement-learning methods achieve superior volume utilization compared with classical heuristics, the learned policies cannot ensure the structural stability of the bin and lack mechanisms for safely reconfiguring the bin when a new item cannot be placed directly. In this work, we propose a novel framework that integrates packing policy with structural stability validation and heuristic planning to overcome these limitations. Specifically, we introduce the concept of Load Bearable Convex Polygon (LBCP), which provides a computationally efficient way to identify stable loading positions that guarantee no bin collapse. Additionally, we present Stable Rearrangement Planning (SRP), a module that rearranges existing items to accommodate new ones while maintaining overall stability. Extensive experiments on standard OBPP benchmarks demonstrate the efficiency and generalizability of our LBCP-based stability validation, as well as the superiority of SRP in finding the effort-saving rearrangement plans. Our method offers a robust and practical solution for automated packing in real-world industrial and logistics applications.
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