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

Jing He, Xinglu Liu, Qiyao Duan, Chan, Wai Kin (Victor) and Mingyao Qi

European Journal of Operational Research, 2023, vol. 305, issue 2, 820-837

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.

Keywords: Machine learning; Puzzle-based storage system; Deep reinforcement learning; Multi-item retrieval; Integer programming (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:305:y:2023:i:2:p:820-837

DOI: 10.1016/j.ejor.2022.03.042

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