Online 3D Bin Packing with Fast Stability Validation and Stable Rearrangement Planning

TL;DR

This paper introduces a new framework for online 3D bin packing that ensures structural stability and efficient rearrangement, combining stability validation with heuristic planning to improve safety and practicality.

Contribution

It proposes the Load Bearable Convex Polygon (LBCP) for stability validation and Stable Rearrangement Planning (SRP) for safe item reconfiguration, advancing online bin packing methods.

Findings

LBCP efficiently identifies stable loading positions.

SRP effectively plans effort-saving rearrangements.

The combined approach outperforms classical heuristics on benchmarks.

Abstract

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.