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IJMLC 2021 Vol.11(3): 242-249 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2021.11.3.1042

Warehouse Management Models Using Artificial Intelligence Technology with Application at Receiving Stage – A Review

Judy X Yang, Lily D Li, and Mohammad G. Rasul

Abstract—This paper reviewed recent literature on inventory management technologies and Artificial Intelligence (AI) applications. The classical Artificial Neural Network (ANN) models and computer vision technology applications for object classification were reviewed in particularly. The challenges of AI technologies in industrial warehouse management, particularly the ANN for solving object classification and counting are discussed. Some researchers reported the use of face recognition, moving vehicle classification and counting, which are easy to recognise objects on the floor or the ground. Other researchers explored the object counting technologies which are used to identify the visible objects on the ground or in images. Although several studies focused on industrial component identification and counting problems, a study on the warehouse receiving stage remains a blank canvas. This paper reviews and analyses current industrial warehouse management developments around AI applications in this field, which may provide a reference for future researchers and end-users for the best modelling approach to this specific problem at the warehouse receiving stage.

Index Terms—Warehouse management, classification, counting, convolutional neuron net.

Judy X Yang, Lily D Li, and Mohammad G. Rasul are with the School of Engineering and Technology, CQUniversity, Rockhampton, QLD 4702, Australia (e-mail: j.yang@cqu.edu.au, l.li@cqu.edu.au, m.rasul@cqu.edu.au).

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Cite: Judy X Yang, Lily D Li, and Mohammad G. Rasul, "Warehouse Management Models Using Artificial Intelligence Technology with Application at Receiving Stage – A Review," International Journal of Machine Learning and Computing vol. 11, no. 3, pp. 242-249, 2021.

Copyright © 2021 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

 

General Information

  • ISSN: 2010-3700 (Online)
  • Abbreviated Title: Int. J. Mach. Learn. Comput.
  • Frequency: Bimonthly
  • DOI: 10.18178/IJMLC
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
  • E-mail: ijmlc@ejournal.net


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