NDLI: Machine-part grouping for cellular manufacturing systems: a neural network approach

Content Provider	IEEE Xplore Digital Library
Author	Kyung-Mi Lee Yamakawa, T. Keon-Myung Lee
Copyright Year	1997
Description	Author affiliation: Sch. of Comput. Sci. & Syst. Eng., Kyushu Inst. of Technol., Iizuka, Japan (Kyung-Mi Lee)
Abstract	The machine cell formation problem is about grouping machines into machine families and parts into part families so as to minimize bottleneck machines, exceptional parts and inter-cell part movements in cellular and flexible manufacturing systems. This paper proposes a new machine cell formation method based on the adaptive Hamming net, which is a neural network model. To see the applicability of the method, this paper shows some experimental results and compares the proposed method with other cell formation methods. From the experiments, we can see that the proposed method can produce good cells for the machine cell formation problem.
Starting Page	575
Ending Page	580
File Size	531840
Page Count	6
File Format	PDF
ISBN	0780337557
DOI	10.1109/KES.1997.619439
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1997-05-21
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Cellular manufacturing Cellular neural networks Neural networks Group technology Batch production systems Collision mitigation Computer science Job production systems Job design Systems engineering and theory
Content Type	Text
Resource Type	Article

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Machine-part grouping for cellular manufacturing systems: a neural network approach