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

Autor:	He, Jing, Liu, Xinglu, Duan, Qiyao, Chan, Wai Kin Victor, Qi, Mingyao
Rok vydání:	2022
Předmět:	Computer Science - Machine Learning Computer Science - Artificial Intelligence Computer Science - Discrete Mathematics Electrical Engineering and Systems Science - Systems and Control Mathematics - Optimization and Control
Druh dokumentu:	Working Paper
Popis:	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. Comment: 32 pages, 13 figures, 5 tables, journal
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2202.03424 Zobrazit plný text záznamu View this record from Arxiv