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Title
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Solving the storage location assignment problem using reinforcement learning
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Author
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Abstract
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| In this work, we deal with the Storage Location Assignment Problem, often referred to as the SLAP, in an E-commerce Distribution Center (EDC). With E-commerce steadily increasing in popularity over the past decades, it has become a key part of the logistics industry. Due to the direct link with the customer, EDC's are forced into a significantly more complex and dynamic order picking process compared to conventional Bulk Distribution Centers. As a result of these challenges, many traditional approaches such as genetic algorithms and rule-based methods reach only suboptimal solutions. We propose the use of Reinforcement Learning (RL) to solve the SLAP, leading to a solution that adapts to dynamically changing environment parameters during runtime. For this purpose, we define a model that transforms the SLAP into a sequential decision making problem. We validate this novel approach by training a state-of-the-art RL algorithm within this model and comparing its results with a benchmark genetic algorithm approach. We conclude that the RL algorithm achieves promising results, surpassing benchmark performance and nearing optimal performance in a small-scale warehouse environment. |
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Language
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English
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Source (book)
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ICMAI 2023 : 8th International Conference on Mathematics and Artificial Intelligence, 7-9 April, 2023, Chongqing, China
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Publication
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New York, N.Y.
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Association for Computing Machinery
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2023
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ISBN
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978-1-4503-9998-2
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978-1-4503-9998-2
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DOI
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10.1145/3594300.3594314
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Volume/pages
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p. 89-95
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Full text (Publisher's DOI)
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