# Machine Learning for the Multi-Dimensional Bin Packing Problem: Literature Review and Empirical Evaluation

arXiv: https://arxiv.org/abs/2312.08103 · HTML: https://mlsanity.com/paper/2312.08103/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 81 references that are in the map.
- First posted on arXiv 13 Dec 2023.

## Builds on (most cited first, 26 of 26)

- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [ShapeNet: An Information-Rich 3D Model Repository](https://mlsanity.com/paper/1512.03012/) (2015)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [High-Dimensional Continuous Control Using Generalized Advantage Estimation](https://mlsanity.com/paper/1506.02438/) (2015)
- [Prioritized Experience Replay](https://mlsanity.com/paper/1511.05952/) (2015)
- [Pointer Networks](https://mlsanity.com/paper/1506.03134/) (2015)
- [Machine Learning for Combinatorial Optimization: a Methodological Tour d'Horizon](https://mlsanity.com/paper/1811.06128/) (2018)
- [Neural Combinatorial Optimization with Reinforcement Learning](https://mlsanity.com/paper/1611.09940/) (2016)
- [Reinforcement Learning for Combinatorial Optimization: A Survey](https://mlsanity.com/paper/2003.03600/) (2020)
- [Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation](https://mlsanity.com/paper/1708.05144/) (2017)
- [Online 3D Bin Packing with Constrained Deep Reinforcement Learning](https://mlsanity.com/paper/2006.14978/) (2020)
- [Solving a New 3D Bin Packing Problem with Deep Reinforcement Learning Method](https://mlsanity.com/paper/1708.05930/) (2017)
- [Ranked Reward: Enabling Self-Play Reinforcement Learning for Combinatorial Optimization](https://mlsanity.com/paper/1807.01672/) (2018)
- [Stable bin packing of non-convex 3D objects with a robot manipulator](https://mlsanity.com/paper/1812.04093/) (2018)
- [Learning Practically Feasible Policies for Online 3D Bin Packing](https://mlsanity.com/paper/2108.13680/) (2021)
- [Attend2Pack: Bin Packing through Deep Reinforcement Learning with Attention](https://mlsanity.com/paper/2107.04333/) (2021)
- [Reinforcement Learning Driven Heuristic Optimization](https://mlsanity.com/paper/1906.06639/) (2019)
- [TAP-Net: Transport-and-Pack using Reinforcement Learning](https://mlsanity.com/paper/2009.01469/) (2020)
- [A Generalized Reinforcement Learning Algorithm for Online 3D Bin-Packing](https://mlsanity.com/paper/2007.00463/) (2020)
- [PackIt: A Virtual Environment for Geometric Planning](https://mlsanity.com/paper/2007.11121/) (2020)
- [Online 3D Bin Packing Reinforcement Learning Solution with Buffer](https://mlsanity.com/paper/2208.07123/) (2022)
- [Small Boxes Big Data: A Deep Learning Approach to Optimize Variable Sized Bin Packing](https://mlsanity.com/paper/1702.04415/) (2017)

## Built on by (most cited first, 4 of 4)

- [Evaluating LLMs for Combinatorial Optimization: One-Phase and Two-Phase Heuristics for 2D Bin-Packing](https://mlsanity.com/paper/2509.22255/) (2025)
- [One4Many-StablePacker: An Efficient Deep Reinforcement Learning Framework for the 3D Bin Packing Problem](https://mlsanity.com/paper/2510.10057/) (2025)
- [Preference-Conditioned Reinforcement Learning for Space-Time Efficient Online 3D Bin Packing](https://mlsanity.com/paper/2603.07800/) (2026)
- [Deep Reinforcement Learning on Item-Compatibility Graphs for One-Dimensional Bin Packing](https://mlsanity.com/paper/2609.25397/) (2026)

## Abstract (arXiv)

The Bin Packing Problem (BPP) is a well-established combinatorial optimization (CO) problem. Since it has many applications in our daily life, e.g. logistics and resource allocation, people are seeking efficient bin packing algorithms. On the other hand, researchers have been making constant advances in machine learning (ML), which is famous for its efficiency. In this article, we first formulate BPP, introducing its variants and practical constraints. Then, a comprehensive survey on ML for multi-dimensional BPP is provided. We further collect some public benchmarks of 3D BPP, and evaluate some online methods on the Cutting Stock Dataset. Finally, we share our perspective on challenges and future directions in BPP. To the best of our knowledge, this is the first systematic review of ML-related methods for BPP.