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

TL;DR

This paper introduces a multi-task selected learning framework for the NP-hard 3D flexible bin packing problem, effectively generating packing sequences and orientations to minimize surface area, outperforming existing heuristics in real-world tests.

Contribution

It proposes a novel multi-task selected learning approach that learns heuristic-like policies for 3D bin packing, improving over traditional heuristics and single-task models.

Findings

Achieves 5.47% cost reduction over existing greedy algorithms.

Outperforms single-task Pointer Network and non-selected multi-task models.

Proven effective on large-scale real-world datasets and online AB tests.

Abstract

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.