By Topic

A data mining based clustering approach to group technology

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Mu-Chen Chen ; Inst. of Commerce Autom. & Manage., Nat. Taipei Univ. of Technol., Taiwan ; Hsiao-Pin Wu ; Chia-Ping Lin

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.

Published in:

Robotics and Automation, 2003. Proceedings. ICRA '03. IEEE International Conference on  (Volume:3 )

Date of Conference:

14-19 Sept. 2003