# A Multi-task Selected Learning Approach for Solving 3D Flexible Bin Packing Problem

arXiv: https://arxiv.org/abs/1804.06896 · HTML: https://mlsanity.com/paper/1804.06896/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Apr 2018.

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

- [Self-play Learning Strategies for Resource Assignment in Open-RAN Networks](https://mlsanity.com/paper/2103.02649/) (2021)
- [A comparative analysis of deep learning models for lung segmentation on X-ray images](https://mlsanity.com/paper/2404.06455/) (2024)
- [Mitigating Dimensionality in 2D Rectangle Packing Problem under Reinforcement Learning Schema](https://mlsanity.com/paper/2409.09677/) (2024)
- [Cartesian Genetic Programming Approach for Designing Convolutional Neural Networks](https://mlsanity.com/paper/2410.00129/) (2024)

## Abstract (arXiv)

A 3D flexible bin packing problem (3D-FBPP) arises from the process of warehouse packing in e-commerce. An online customer's order usually contains several items and needs to be packed as a whole before shipping. In particular, 5% of tens of millions of packages are using plastic wrapping as outer packaging every day, which brings pressure on the plastic surface minimization to save traditional logistics costs. Because of the huge practical significance, we focus on the issue of packing cuboid-shaped items orthogonally into a least-surface-area bin. The existing heuristic methods for classic 3D bin packing don't work well for this particular NP-hard problem and designing a good problem-specific heuristic is non-trivial. In this paper, rather than designing heuristics, we propose a novel multi-task framework based on Selected Learning to learn a heuristic-like policy that generates the sequence and orientations of items to be packed simultaneously. Through comprehensive experiments on a large scale real-world transaction order dataset and online AB tests, we show: 1) our selected learning method trades off the imbalance and correlation among the tasks and significantly outperforms the single task Pointer Network and the multi-task network without selected learning; 2) our method obtains an average 5.47% cost reduction than the well-designed greedy algorithm which is previously used in our online production system.