Abstract
The crux problem of group technology (GT) is the identification of part families requiring similar manufacturing processes and the rearrangement of machines to minimize the number of parts that visit more than one machine cell. This paper presents an improved method for part family formation, machine cell identification, bottleneck machine detection and the natural cluster generation using a self-organizing neural network. In addition, the generalization ability of the neural network makes it possible to assign the new parts to the existing machine cells without repeating the entire computational process. A computer program is developed to illustrate the effectiveness of this heuristic method by comparing it with the optimal technique for large-scale problems.
| Original language | English |
|---|---|
| Pages (from-to) | 325-332 |
| Number of pages | 8 |
| Journal | Journal of Intelligent Manufacturing |
| Volume | 3 |
| Issue number | 5 |
| DOIs | |
| Publication status | Published - 1992 Oct 1 |
| Externally published | Yes |
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Keywords
- cellular manufacturing system
- cluster analysis
- generalization
- Group technology
- neural network
ASJC Scopus subject areas
- Industrial and Manufacturing Engineering
- Artificial Intelligence
Cite this
A self-organizing neural network approach for the design of cellular manufacturing systems. / Lee, Hong Chul; Malavé, César O.; Ramachandran, Satheesh.
In: Journal of Intelligent Manufacturing, Vol. 3, No. 5, 01.10.1992, p. 325-332.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - A self-organizing neural network approach for the design of cellular manufacturing systems
AU - Lee, Hong Chul
AU - Malavé, César O.
AU - Ramachandran, Satheesh
PY - 1992/10/1
Y1 - 1992/10/1
N2 - The crux problem of group technology (GT) is the identification of part families requiring similar manufacturing processes and the rearrangement of machines to minimize the number of parts that visit more than one machine cell. This paper presents an improved method for part family formation, machine cell identification, bottleneck machine detection and the natural cluster generation using a self-organizing neural network. In addition, the generalization ability of the neural network makes it possible to assign the new parts to the existing machine cells without repeating the entire computational process. A computer program is developed to illustrate the effectiveness of this heuristic method by comparing it with the optimal technique for large-scale problems.
AB - The crux problem of group technology (GT) is the identification of part families requiring similar manufacturing processes and the rearrangement of machines to minimize the number of parts that visit more than one machine cell. This paper presents an improved method for part family formation, machine cell identification, bottleneck machine detection and the natural cluster generation using a self-organizing neural network. In addition, the generalization ability of the neural network makes it possible to assign the new parts to the existing machine cells without repeating the entire computational process. A computer program is developed to illustrate the effectiveness of this heuristic method by comparing it with the optimal technique for large-scale problems.
KW - cellular manufacturing system
KW - cluster analysis
KW - generalization
KW - Group technology
KW - neural network
UR - http://www.scopus.com/inward/record.url?scp=0000251299&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0000251299&partnerID=8YFLogxK
U2 - 10.1007/BF01577273
DO - 10.1007/BF01577273
M3 - Article
VL - 3
SP - 325
EP - 332
JO - Journal of Intelligent Manufacturing
JF - Journal of Intelligent Manufacturing
SN - 0956-5515
IS - 5
ER -