An Improved Neural Network Model for Manufacturing Cell Formation

• Main Author: CHU, Chao-Hsien
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 1997
• Subjects: Neural networks; Unsupervised learning; Interactive activation and competitive learning; Cellular manufacturing; Computer Sciences; Management Information Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/1769; http://dx.doi.org/10.1016/S0167-9236(97)00015-8
• Institution: Singapore Management University

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.