A Deep Learning Approach to Minimize Retrieval Time in Shuttle-Based Storage Systems
Résumé
Improvement of picking performances in automated warehouse is influenced by the assignment of articles to storage locations. This problem is known as the Storage Location Assignment Problem (SLAP). In this paper, we present a deep learning method to assign articles to storage locations inside a shuttles-based storage and retrieval system (SBS/RS). We introduce the architecture of our a LSTM-based model and the public dataset used. Finally, we compare the retrieval time of articles provided by our model against other allocation methods.
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