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

arXiv: https://arxiv.org/abs/1708.05930 · HTML: https://mlsanity.com/paper/1708.05930/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Aug 2017.

## Built on by (most cited first, 30 of 31)

- [Hindsight Learning for MDPs with Exogenous Inputs](https://mlsanity.com/paper/2207.06272/) (2022)
- [Learning Practically Feasible Policies for Online 3D Bin Packing](https://mlsanity.com/paper/2108.13680/) (2021)
- [Learning to Control Local Search for Combinatorial Optimization](https://mlsanity.com/paper/2206.13181/) (2022)
- [Attend2Pack: Bin Packing through Deep Reinforcement Learning with Attention](https://mlsanity.com/paper/2107.04333/) (2021)
- [Automated Algorithm Selection: from Feature-Based to Feature-Free Approaches](https://mlsanity.com/paper/2203.13392/) (2022)
- [Learning Physically Realizable Skills for Online Packing of General 3D Shapes](https://mlsanity.com/paper/2212.02094/) (2022)
- [Adjustable Robust Reinforcement Learning for Online 3D Bin Packing](https://mlsanity.com/paper/2310.04323/) (2023)
- [Online 3D Bin Packing Reinforcement Learning Solution with Buffer](https://mlsanity.com/paper/2208.07123/) (2022)
- [Planning Irregular Object Packing via Hierarchical Reinforcement Learning](https://mlsanity.com/paper/2211.09382/) (2022)
- [(X)-evolve: Solution space evolution powered by large language models](https://mlsanity.com/paper/2508.07932/) (2025)
- [Optimizing Tensor Network Contraction Using Reinforcement Learning](https://mlsanity.com/paper/2204.09052/) (2022)
- [Machine Learning for the Multi-Dimensional Bin Packing Problem: Literature Review and Empirical Evaluation](https://mlsanity.com/paper/2312.08103/) (2023)
- [Deliberate Planning of 3D Bin Packing on Packing Configuration Trees](https://mlsanity.com/paper/2504.04421/) (2025)
- [Neural Packing: from Visual Sensing to Reinforcement Learning](https://mlsanity.com/paper/2311.09233/) (2023)
- [RASP: Revisiting 3D Anamorphic Art for Shadow-Guided Packing of Irregular Objects](https://mlsanity.com/paper/2504.02465/) (2025)
- [Deep-Steiner: Learning to Solve the Euclidean Steiner Tree Problem](https://mlsanity.com/paper/2209.09983/) (2022)
- [Solving Logistic-Oriented Bin Packing Problems Through a Hybrid Quantum-Classical Approach](https://mlsanity.com/paper/2308.02787/) (2023)
- [ASAP: Exploiting the Satisficing Generalization Edge in Neural Combinatorial Optimization](https://mlsanity.com/paper/2501.17377/) (2025)
- [A Hierarchical Bin Packing Framework with Dual Manipulators via Heuristic Search and Deep Reinforcement Learning](https://mlsanity.com/paper/2506.01628/) (2025)
- [Joint Cluster Head Selection and Trajectory Planning in UAV-Aided IoT Networks by Reinforcement Learning with Sequential Model](https://mlsanity.com/paper/2112.00333/) (2021)
- [A Data-Driven Column Generation Algorithm For Bin Packing Problem in Manufacturing Industry](https://mlsanity.com/paper/2202.12466/) (2022)
- [A Virtual-Force Based Swarm Algorithm for Balanced Circular Bin Packing Problems](https://mlsanity.com/paper/2306.01021/) (2023)
- [Using Reinforcement Learning for the Three-Dimensional Loading Capacitated Vehicle Routing Problem](https://mlsanity.com/paper/2307.12136/) (2023)
- [HERB: Human-augmented Efficient Reinforcement learning for Bin-packing](https://mlsanity.com/paper/2504.16595/) (2025)
- [Towards VM Rescheduling Optimization Through Deep Reinforcement Learning](https://mlsanity.com/paper/2505.17359/) (2025)
- [One4Many-StablePacker: An Efficient Deep Reinforcement Learning Framework for the 3D Bin Packing Problem](https://mlsanity.com/paper/2510.10057/) (2025)
- [Effective Online 3D Bin Packing with Lookahead Parcels Using Monte Carlo Tree Search](https://mlsanity.com/paper/2601.02649/) (2026)
- [Differentiable Packing of Irregular 3D Objects with Adaptive Container Estimation](https://mlsanity.com/paper/2606.16333/) (2026)
- [Operationally Guided Placement-Aware Learning for Industrial Online 3D Bin Packing](https://mlsanity.com/paper/2607.28257/) (2026)
- [DGA2D: Directed Graph-Guided Automated Algorithm Design with Large Language Models](https://mlsanity.com/paper/2608.00700/) (2026)

## Abstract (arXiv)

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.