1
Associate professor, Ferdowsi University of Mashhad.
2
PhD. Student, Ferdosi University.
Abstract
The most important problem during implementation of group technology in industrial units is firstly, part family formation and grouping parts according to it and secondly, achieving consistency during the time. There are different methods for grouping of parts. When we use part attributes for grouping. The first and the most important stage is designing of a part coding system and identification of parts on the basis of it. This paper investigated application of a back propagation neural network in part grouping for connectors pins and their attributes.And its results were compared to grouping results of K-means cluster analysis, similarity coefficient method and ROC method. Finally the results showed the capability of neural networks for parts grouping on the basis of their attributes and preference of neural networks to K-meanscluster analysis.
Pooya, A. R., & Javan Rad, E. (2014). Implementation of Neural Networks in Group Technology and Its Comparison to the Results of K-means, Similarity Coefficient Method and Rank Order Clustering. Journal of Industrial Management Perspective, 3(4), 39-62.
MLA
Ali Reza Pooya; Ehsan Javan Rad. "Implementation of Neural Networks in Group Technology and Its Comparison to the Results of K-means, Similarity Coefficient Method and Rank Order Clustering". Journal of Industrial Management Perspective, 3, 4, 2014, 39-62.
HARVARD
Pooya, A. R., Javan Rad, E. (2014). 'Implementation of Neural Networks in Group Technology and Its Comparison to the Results of K-means, Similarity Coefficient Method and Rank Order Clustering', Journal of Industrial Management Perspective, 3(4), pp. 39-62.
VANCOUVER
Pooya, A. R., Javan Rad, E. Implementation of Neural Networks in Group Technology and Its Comparison to the Results of K-means, Similarity Coefficient Method and Rank Order Clustering. Journal of Industrial Management Perspective, 2014; 3(4): 39-62.
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