DocumentCode :
315590
Title :
Machine-part grouping for cellular manufacturing systems: a neural network approach
Author :
Lee, Kyung-Mi ; Yamakawa, Takeshi ; Lee, Keon-Myung
Author_Institution :
Sch. of Comput. Sci. & Syst. Eng., Kyushu Inst. of Technol., Iizuka, Japan
Volume :
2
fYear :
1997
fDate :
27-23 May 1997
Firstpage :
575
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
Keywords :
adaptive systems; batch processing (industrial); feedforward neural nets; flexible manufacturing systems; production engineering computing; adaptive Hamming net; bottleneck machines; cellular manufacturing systems; exceptional parts; flexible manufacturing systems; group technology; inter-cell part movements; machine cell formation problem; machine families; machine-part grouping; minimization; neural network model; part families; Batch production systems; Cellular manufacturing; Cellular neural networks; Collision mitigation; Computer science; Group technology; Job design; Job production systems; Neural networks; Systems engineering and theory;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Knowledge-Based Intelligent Electronic Systems, 1997. KES '97. Proceedings., 1997 First International Conference on
Conference_Location :
Adelaide, SA
Print_ISBN :
0-7803-3755-7
Type :
conf
DOI :
10.1109/KES.1997.619439
Filename :
619439
Link To Document :
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