An Improved Neural Network Model for Manufacturing Cell Formation

Publication Type

Journal Article

Publication Date

1997

Abstract

With structures inspired by the structure of the human brain and nervous system, neural networks provide a unique computational architecture for addressing problems that are difficult or impossible to solve with traditional methods. In this paper, an unsupervised neural network model, based upon the interactive activation and competition (IAC) learning paradigm, is proposed as a good alternative decision-support tool to solve the cell-formation problem of cellular manufacturing. The proposed implementation is easy to use and can simultaneously form part families and machine cells, which is very difficult or impossible to achieve by conventional methods. Our computational experience shows that the procedure is fairly efficient and robust, and it can consistently produce good clustering results.

Keywords

Neural networks, Unsupervised learning, Interactive activation and competitive learning, Cellular manufacturing

Discipline

Computer Sciences | Management Information Systems

Research Areas

Information Systems and Management

Publication

Decision Support Systems

Volume

20

Issue

4

First Page

279

Last Page

295

ISSN

0167-9236

Identifier

10.1016/S0167-9236(97)00015-8

Publisher

Elsevier

Additional URL

http://dx.doi.org/10.1016/S0167-9236(97)00015-8

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