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Abstract
Background: In the context of Warehouse Management Systems, knowledge related to motion trajectory prediction methods utilizing machine learning techniques seems to be scattered and fragmented. Objective: This study seeks to fill this research gap by using a systematic literature review approach. Methods: Based on the data collected from Google Scholar, a systematic literature review was performed, covering the period from 2016 to 2023. The review was driven by a protocol that comprehends inclusion and exclusion criteria to identify relevant papers. Results: Considering the Warehouse Management Systems, five categories of motion trajectory prediction methods have been identified: Deep Learning methods, probabilistic methods, methods for solving the Travelling-Salesman problem (TSP), algorithmic methods, and others. Specifically, the performed analysis also provides the research community with an overview of the state-of-the-art methods, which can further stimulate researchers and practitioners to enhance existing and develop new ones in this field.
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Authors (3)
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Jakub Belter
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Marek Hering
Cite as
Full text
- Publication version
- Accepted or Published Version
- DOI:
- Digital Object Identifier (open in new tab) 10.3390/app13179780
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Details
- Category:
- Articles
- Type:
- artykuły w czasopismach
- Published in:
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Applied Sciences-Basel
no. 13,
ISSN: 2076-3417 - Language:
- English
- Publication year:
- 2023
- Bibliographic description:
- Belter J., Hering M., Weichbroth P.: Motion Trajectory Prediction in Warehouse Management Systems: A Systematic Literature Review// Applied Sciences-Basel -,iss. 13 (17) (2023),
- DOI:
- Digital Object Identifier (open in new tab) 10.3390/app13179780
- Sources of funding:
-
- Free publication
- Verified by:
- Gdańsk University of Technology
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