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2021 | OriginalPaper | Buchkapitel

Machine Learning for Storage Location Prediction in Industrial High Bay Warehouses

verfasst von : Fabian Berns, Timo Ramsdorf, Christian Beecks

Erschienen in: Pattern Recognition. ICPR International Workshops and Challenges

Verlag: Springer International Publishing

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Abstract

Global trade and logistics require efficient management of the scarce resource of storage locations. In order to adequately manage that resource in a high bay warehouse, information regarding the overall logistics processes need to be considered, while still enabling human stakeholders to keep track of the decision process and utilizing their non-digitized, domain-specific, expert knowledge. Although a plethora of machine learning models gained high popularity in many industrial sectors, only those models that provide a transparent perspective on their own inner decision procedures are applicable for a sensitive domain like logistics. In this paper, we propose the application of machine learning for efficient data-driven storage type classification in logistics. In order to reflect this research problem in practice, we used production data from a warehouse at a large Danish retailer. We evaluate and discuss the proposed solution and its different manifestations in the given logistics context.

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Titel: Machine Learning for Storage Location Prediction in Industrial High Bay Warehouses
verfasst von: Fabian Berns
Timo Ramsdorf
Christian Beecks
Verlag: Springer International Publishing
Buch: Pattern Recognition. ICPR International Workshops and Challenges
Print ISBN: 978-3-030-68798-4

Electronic ISBN: 978-3-030-68799-1

Copyright-Jahr: 2021
DOI: https://doi.org/10.1007/978-3-030-68799-1_47

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