期刊
TECHNOLOGIES
卷 10, 期 6, 页码 -出版社
MDPI
DOI: 10.3390/technologies10060129
关键词
warehouse management; logistics; dynamic storage location assignment; reinforcement learning; deep learning; artificial intelligence
This paper presents a real-world use case of using deep reinforcement learning to solve the dynamic storage location assignment problem in the warehousing industry. The study found that this method can decrease transportation costs compared to traditional manual classification methods.
The warehousing industry is faced with increasing customer demands and growing global competition. A major factor in the efficient operation of warehouses is the strategic storage location assignment of arriving goods, termed the dynamic storage location assignment problem (DSLAP). This paper presents a real-world use case of the DSLAP, in which deep reinforcement learning (DRL) is used to derive a suitable storage location assignment strategy to decrease transportation costs within the warehouse. The DRL agent is trained on historic data of storage and retrieval operations gathered over one year of operation. The evaluation of the agent on new data of two months shows a 6.3% decrease in incurring costs compared to the currently utilized storage location assignment strategy which is based on manual ABC-classifications. Hence, DRL proves to be a competitive solution alternative for the DSLAP and related problems in the warehousing industry.
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