2022/08/15 by Aaron Valero Puche, Sukhan Lee, Puche, Aaron Valero +1 · 1 citation
Engineering · #Advanced Manufacturing and Logistics Optimization #Artificial Intelligence (cs.AI) #Assembly Line Balancing Optimization #FOS: Computer and information sciences #Optimization and Packing Problems #Robotics (cs.RO)
paper · pdf · doi:10.48550/arxiv.2208.07123
openalex publication_date 2022/08/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The 3D Bin Packing Problem (3D-BPP) is one of the most demanded yet challenging problems in industry, where an agent must pack variable size items delivered in sequence into a finite bin with the aim to maximize the space utilization. It represents a strongly NP-Hard optimization problem such that no solution has been offered to date with high performance in space utilization. In this paper, we present a new reinforcement learning (RL) framework for a 3D-BPP solution for improving performance. First, a buffer is introduced to allow multi-item action selection. By increasing the degree of freedom in action selection, a more complex policy that results in better packing performance can be derived. Second, we propose an agnostic data augmentation strategy that exploits both bin item symmetries for improving sample efficiency. Third, we implement a model-based RL method adapted from the popular algorithm AlphaGo, which has shown superhuman performance in zero-sum games. Our adaptation is capable of working in single-player and score based environments. In spite of the fact that AlphaGo versions are known to be computationally heavy, we manage to train the proposed framework with a single thread and GPU, while obtaining a solution that outperforms the state-of-the-art results in space utilization.