在群組技術（group technology ）的觀念下，對於如何完成單元形成問題(cell formation)有兩個基本的程序。一是工件族的形成，另一個則是機械群的形成。在本篇文章中，我們將使用FRC 群集演算法(Dav´e and Sen, 2002)去解決此工件族與機械群的形成步驟。然後利用群組效率指標( Chandrasekharan and Rajagopalan, 1986)去決定出最佳的單元形成結果。此外我們也在本文中提出一個適用於符號型資料與模糊型資料的非相似性距離測度公式。從實例的研究探討中，顯示此模式可得到良好的實例驗證結果。

Cellular manufacturing is a useful way to improve the overall manu- facturing performance.The key step in designing any cellular manufactur- ing system is the identification of part-families and machine-groups for the creation of cells, in which the parts in each cell are processed with the minimum movement in to other cells. There are two basic procedures for cell formation (CF) in group technology. One is part-family formation and the other is machine-cell formation. In this paper, we apply the fuzzy relational data clustering (FRC) algorithm (see Dav´e and Sen, 2002) to form part-family and machinecell. Then we use the grouping efficiency (see Chandrasekharan and Rajagopalan, 1986)to find the optimal CF. Besides，we give a modified dissimilarity measure for symbolic and fuzzy data.The real case study
shows that the proposed approach performs well.

參考文獻
[1] Chandrasekharan, M.P., and Rajagopalan, R., “ZODIAC-an
algorithm for concurrent formation of part-families and
machine-cells,” International Journal of Production
Research, 25, 835-850, (1987).
[2] Chan, F.T.S., Mak, K.L., Luong, L.H.S., and Ming,
X.G.,“Machine-component grouping using genetic
algorithm,”Robotics & Computer-Integrated
manufacturing, 14, 339-346,(1998).
[3] Diday, E., Gowda,K.C. Symbolic clusteringusinga new
dissimilarity measure, Pattern Recognition 24 (6)
(1991) 567–578.
[4] Diday, E., Gowda,K.C., Symbolic clusteringusinga new
similarity measure, IEEE Trans. System Man Cybernet.
22 (1992) 368–378.
[5] Davé,R.N., Sen, S., Robust Fuzzy Clustering of
Relational Data, IEEE Trans. Systems, vol.10,
pp.713-726 no. 6, 12,2002
[6] El-Sonbaty, Y. A. and Ismail ,M.A.Ismail., Fuzzy
clustering for symbolic data. IEEE Trans. Systems,
6(2) (1988) 195-204
[7] Hathaway, R. J., Devenport, J. W., and Bezdek, J.C.,
“Relation duals of the c-means clustering algorithms,
Pattern Recog., vol. 22, pp. 205–212, 1989
[8] Hathaway, R.J., Bezdek, J.C., Pedrycz ,W., A parametric
model for fusing heteroeneous fuzzy data, IEEE Trans.
Fuzzy Systems 4 (3) (1996) 270–281.
[9] Kaufman, L. and Rousseeuw,P. J., Finding Groups in Data:
An Introduction to ClusterAnalysis. New York: Wiley,
1990.
[10] O.Felix Offodile and Abraham Mehrez,and John Grznar
Kent State University, Kent, Ohio“Cellular
Manufacturing：A Taxonomic Review Framework”Journal
of Manufacturing Systems Volume 13/No.3
[11] Onwubolu, G. C., and Songore, V., “A tabu search
approach to cellular manufacturing systems,”
Production Planning & Control,11, 153-164, (2000).
[12] Yang, M.S.,A survey of fuzzy clustering,Mathematical
and Computer Modeling,vol18,pp.1-16,1993
[13] Yang, M.S., Ko,C.H., On a class of fuzzy c-numbers
clustering procedures for fuzzy data, Fuzzy Sets and
Systems84 (1996) 49–60.
[14] Yang, M.S., Hwang, P.Y. and Chen, D.H.,Fuzzy clustering
algorithms for mixed feature variables, Fuzzy Sets
and Systems 141 (2004) 301–317.
[15] Yang, M.S., Hung, W.L. and Cheng, F.C., Mixed-variable
fuzzy clustering approach to part family and machine
cell formation for GT applications, International
Journal of Production economics 103 ，185-198(2006)
[16] Wemmerlov, U. and Hyer, N.L., Cellular manufacturing
in the U.S. industry：a survey of users,
”International Journal of Production Research, 27,
no.9,1511-1530 (1989).
[17]Wemmerlov, U. and Hyer, N.L., Reserch issues in
cellular manufacturing,International Journal of
Production Research,25, no.9, 413-431, (1987).
[18] Zandeh,L.A., Fuzzy sets,Information and Control,
vol.8,pp.338-353,1965
[19] 吳社邦, 群組技術手冊 CS-1 分類與編碼系統 ,中興管理顧問公司,
1985,2
[20] 劉孝平, 以「類似技術」建構製造工廠核心製程知識系統之研究機器
群形成過程修正法---綜合指標法及實例研究,中原大學企業管理學系碩
士學位論文,1992.6
[21] KHK STOCK GEARS：
http://www.khkgears.co.jp/tw/index.html