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.
T. Ramsdorf—This research has been conducted, while the author was affiliated with University of Münster.
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Notes
- 1.
For the sake of simplicity, we intentionally generalized the intricacies of a WMS and left out detailed concepts like the Material Flow System (MFS) .
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Berns, F., Ramsdorf, T., Beecks, C. (2021). Machine Learning for Storage Location Prediction in Industrial High Bay Warehouses. In: Del Bimbo, A., et al. Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Science(), vol 12664. Springer, Cham. https://doi.org/10.1007/978-3-030-68799-1_47
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