Title :
A stochastic unsupervised competitive learning algorithm for the design of cellular manufacturing systems
Author :
Ponnambalam, S.G. ; Aravindan, P.
Author_Institution :
Dept. of Mech. & Production Eng., PSG Coll. of Technol., Coimbatore, India
fDate :
27 Jun-2 Jul 1994
Abstract :
Cellular manufacturing systems (CMS), an application of group technology (GT) derives the benefit of cost advantage of mass production effect for multi-product small-lot-sized production. There are many applications to this concept of group technology in engineering manufacture. In CMS, machine tools are grouped into cells and each one of them is generally dedicated to the manufacture of a part-family. In this paper, a stochastic unsupervised learning algorithm (SUCLA) has been developed. This is a two-layer feedforward network trained with competitive learning. The proposed algorithm is used for the simultaneous formation of machine cells and part families. This model is tested with a considerable size of data sets available in the open literature. This algorithm has been found to be a flexible and powerful tool for the design of CMS
Keywords :
computer aided production planning; feedforward neural nets; multilayer perceptrons; unsupervised learning; cellular manufacturing systems; cost advantage; group technology; machine cells; mass production; multi-product small-lot-sized production; part families; stochastic unsupervised competitive learning algorithm; two-layer feedforward network; Algorithm design and analysis; Cellular manufacturing; Collision mitigation; Group technology; Machine tools; Mass production; Production systems; Stochastic processes; Testing; Unsupervised learning;
Conference_Titel :
Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
0-7803-1901-X
DOI :
10.1109/ICNN.1994.374266
