A SELF-ORGANIZING NEURAL NETWORK APPROACH FOR THE DESIGN OF CELLULAR MANUFACTURING SYSTEMS Academic Article

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. 1992 Chapman & Hall.

authors

• Malave, Cesar

published proceedings

• JOURNAL OF INTELLIGENT MANUFACTURING

author list (cited authors)

• LEE, H., MALAVE, C. O., & RAMACHANDRAN, S.

citation count

• 35

complete list of authors

• LEE, H||MALAVE, CO||RAMACHANDRAN, S

publication date

• October 1992

publisher

• Springer Nature  Publisher

published in

• Journal of Intelligent Manufacturing  Journal

keywords

• Cellular Manufacturing System
• Cluster Analysis
• Generalization
• Group Technology
• Neural Network

Digital Object Identifier (DOI)

• 10.1007/BF01577273

start page

• 325

end page

• 332

volume

• 3

issue

• 5

URL

• http%3A%2F%2Fdx.doi.org%2F10.1007%2Fbf01577273