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Solving a New 3D Bin Packing Problem with Deep Reinforcement Learning Method

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arxiv 1708.05930 v1 pith:JZL3MYGH submitted 2017-08-20 cs.AI

Haoyuan Hu , Xiaodong Zhang , Xiaowei Yan , Longfei Wang , Yinghui Xu This is my paper · ORCID
classification cs.AI
keywords itemsareamethodproblemsurfacesequencedeeplearning
verification ladder T0 review T1 audit T2 compute T3 formal
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In this paper, a new type of 3D bin packing problem (BPP) is proposed, in which a number of cuboid-shaped items must be put into a bin one by one orthogonally. The objective is to find a way to place these items that can minimize the surface area of the bin. This problem is based on the fact that there is no fixed-sized bin in many real business scenarios and the cost of a bin is proportional to its surface area. Our research shows that this problem is NP-hard. Based on previous research on 3D BPP, the surface area is determined by the sequence, spatial locations and orientations of items. Among these factors, the sequence of items plays a key role in minimizing the surface area. Inspired by recent achievements of deep reinforcement learning (DRL) techniques, especially Pointer Network, on combinatorial optimization problems such as TSP, a DRL-based method is applied to optimize the sequence of items to be packed into the bin. Numerical results show that the method proposed in this paper achieve about 5% improvement than heuristic method.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Differentiable Packing of Irregular 3D Objects with Adaptive Container Estimation

    cs.CV 2026-06 unverdicted novelty 6.0 of 10

    A differentiable optimization framework jointly tunes 6N object poses and three container dimensions via six mesh-based losses and adaptive squeezing, producing 11-32% smaller containers than baselines for N=100.

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

    cs.LG 2025-10 conditional novelty 6.0 of 10

    One deep RL model for 3D bin packing generalizes to unseen bin dimensions and enforces stability constraints, via a weighted loading-rate/height-difference reward and entropy-controlled PPO.

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

    cs.AI 2026-07 conditional novelty 5.0 of 10

    Operationally guided multi-anchor EMS candidates plus a 15-feature xLSTM ranker raise online industrial 3D packing density to 0.49, with 15.1% from exposure and 6.3% from learned ranking.

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