Reinforcement learning for multi-item retrieval in the puzzle-based storage system

• URL: http://arxiv.org/abs/2202.03424v1
• Date: Sat, 5 Feb 2022 12:39:21 GMT
• Title: Reinforcement learning for multi-item retrieval in the puzzle-based storage system
• Authors: Jing He, Xinglu Liu, Qiyao Duan, Wai Kin Victor Chan, Mingyao Qi
• Abstract summary: This work develops a deep reinforcement learning algorithm to solve the multi-item retrieval problem in the puzzle-based storage system. Extensive numerical experiments demonstrate that the reinforcement learning approach can yield high-quality solutions. A conversion algorithm and a decomposition framework are proposed to handle simultaneous movement and large-scale instances.
• Score: 0.694936386455667
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Nowadays, fast delivery services have created the need for high-density warehouses. The puzzle-based storage system is a practical way to enhance the storage density, however, facing difficulties in the retrieval process. In this work, a deep reinforcement learning algorithm, specifically the Double&Dueling Deep Q Network, is developed to solve the multi-item retrieval problem in the system with general settings, where multiple desired items, escorts, and I/O points are placed randomly. Additionally, we propose a general compact integer programming model to evaluate the solution quality. Extensive numerical experiments demonstrate that the reinforcement learning approach can yield high-quality solutions and outperforms three related state-of-the-art heuristic algorithms. Furthermore, a conversion algorithm and a decomposition framework are proposed to handle simultaneous movement and large-scale instances respectively, thus improving the applicability of the PBS system.

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