A data mining based clustering approach to group technology

Mu-Chen Chen*, Hsiao Pin Wu, Chia Ping Lin

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

1 Scopus citations

Abstract

Cellular manufacturing is an essential application of group technology (GT) in which families of parts are produced in manufacturing cells. This paper describes the development of a cell formation approach based on association rule mining and 0-1 integer programming. It is valuable to find the important associations among machines such that the occurrence of some machines in a machine cell will cause the occurrence of other machines in the same cell. A clustering model using the discovered association data is formulated to maximize the closeness measures among machines within each cell. From the results of three medium-sized problems, the proposed approach shows its ability to find quality solutions of cell formation problems.

Original languageEnglish
Pages (from-to)3554-3558
Number of pages5
JournalProceedings - IEEE International Conference on Robotics and Automation
Volume3
DOIs
StatePublished - 9 Dec 2003
Event2003 IEEE International Conference on Robotics and Automation - Taipei, Taiwan
Duration: 14 Sep 200319 Sep 2003

Keywords

  • 0-1 integer programming
  • Association rule
  • Cellular manufacturing

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