Reinforcement learning for multi-item retrieval in the puzzle-based storage system. (arXiv:2202.03424v1 [cs.LG])

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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