One model Packs Thousands of Items with Recurrent Conditional Query Learning

TL;DR

This paper introduces Recurrent Conditional Query Learning (RCQL), a neural method that efficiently solves 2D and 3D packing problems by handling variable problem sizes and outperforming existing heuristics in space utilization.

Contribution

The paper presents a novel RCQL model with a recurrent encoder and conditional queries, enabling effective learning for complex packing tasks across different problem sizes.

Findings

RCQL outperforms baseline methods in space utilization ratio.

Reduces bin gap ratio by up to 7.84% in 3D packing.

Achieves 5.64% higher space utilization in large-scale SPPs.

Abstract

Recent studies have revealed that neural combinatorial optimization (NCO) has advantages over conventional algorithms in many combinatorial optimization problems such as routing, but it is less efficient for more complicated optimization tasks such as packing which involves mutually conditioned action spaces. In this paper, we propose a Recurrent Conditional Query Learning (RCQL) method to solve both 2D and 3D packing problems. We first embed states by a recurrent encoder, and then adopt attention with conditional queries from previous actions. The conditional query mechanism fills the information gap between learning steps, which shapes the problem as a Markov decision process. Benefiting from the recurrence, a single RCQL model is capable of handling different sizes of packing problems. Experiment results show that RCQL can effectively learn strong heuristics for offline and online strip packing problems (SPPs), outperforming a wide range of baselines in space utilization ratio. RCQL reduces the average bin gap ratio by 1.83% in offline 2D 40-box cases and 7.84% in 3D cases compared with state-of-the-art methods. Meanwhile, our method also achieves 5.64% higher space utilization ratio for SPPs with 1000 items than the state of the art.