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Chanintorn Jittawiriyanukoon and Vilasinee Srisarkun


Operations in clustering and racking systems (CRS) are labor intensive warehouse management. Operators have been looking for an automated approach to escalate zoning efficiency. Since space management affects the efficiency of inter-warehouse operation, this article proposes similarity algorithms upon trained image data from machine learning (ML) to categorize products. This eases product placement and space planning to automate the smart warehouse. First, upon the request of new placements, this study manipulates the analysis of image similarity to identify the products’ cluster. In accordance with the ML based training among the image lists, the similarity based on distance vector analysis is applied for the choices of storage location in the warehouse. Lastly, the proposed method was determined by using the simulation with the use-case images. The results confirm that it can sort out the decision in clustering and racking operations.

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How to Cite
Chanintorn Jittawiriyanukoon and Vilasinee Srisarkun. (2022). A CLUSTERING AND RACKING SYSTEMS USING IMAGE SIMILARITY AND MACHINE LEARNING. Journal of East China University of Science and Technology, 65(4), 163–172. Retrieved from http://hdlgdxxb.info/index.php/JE_CUST/article/view/374
Author Biography

Chanintorn Jittawiriyanukoon and Vilasinee Srisarkun

Chanintorn Jittawiriyanukoon received the B.Eng degree in electrical engineering from Kasetsart University, Bangkok, Thailand in 1984, M.Eng. and D.Eng. degrees in data communication from Osaka University, Osaka, Japan in 1987 and 1990 respectively. He is presently an assistant professor in Information Technology and also a Director of Master program in Information Technology Management at Graduate School of Business and Advanced Technology Management, Assumption University, Bangkok, Thailand.  His research focuses include big data curation, computer vision, fast routing algorithm, Adaptive Rate Control (ARC) algorithm, queuing network analysis and high-speed-network performance evaluation.