Dynamic grouping of parts in flexible manufacturing systems - a self-organizing neural networks approach

Uday Kulkarni, Melody Y. Kiang

Research output: Contribution to journalArticlepeer-review

33 Scopus citations


Artificial Intelligence (AI) has recently been recognized as a worthwhile tool for supporting manufacturing operations. This paper reviews AI-related approaches to Group Technology (GT) and presents the Self-Organizing Map (SOM) network, a special type of neural networks, as an intelligent tool for grouping parts and machines. SOM can learn from comples, multi-dimensional data and transform them into visually decipherable clusters. What sets this technique apart from others in GT is that SOM offers the flexibility of choosing from multiple grouping alternatives. SOM can be used in a dynamic situation where quick response to changes in part designs, process plans, or manufacturing conditions is essential, and thus it can be more easily integrated into a Flexible Manufacturing System. The paper proposes a framework of an intelligent system that integrates the neural networks approach and a knowledge-based system to provide decision supporting functions.

Original languageEnglish (US)
Pages (from-to)192-212
Number of pages21
JournalEuropean Journal of Operational Research
Issue number1
StatePublished - Jul 7 1995


  • Artificial intelligence
  • Decision support systems
  • Group technology
  • Neural nets
  • Self-organizing map

ASJC Scopus subject areas

  • General Computer Science
  • Modeling and Simulation
  • Management Science and Operations Research
  • Information Systems and Management


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