Machine Cell Formation Based on a New Similarity Coefficient

Document Type : Research Paper

Authors

1 Department of Mechanical Engineering, Helwan University, Helwan, Cairo, 11792, EGYPT

2 Department of Industrial Engineering, University of Houston, Houston, TX, 77204, USA

3 Department of Industrial Engineering, University of Louisville, Louisville, KY, 40292, USA.

Abstract

One of the designs of cellular manufacturing systems (CMS) requires that a machine population be partitioned into machine cells. Numerous methods are available for clustering machines into machine cells. One method involves using a similarity coefficient. Similarity coefficients between machines are not absolute, and they still need more attention from researchers. Although there are a number of similarity coefficients in the literature, they do not always incorporate the important properties of a similarity coefficient satisfactorily. These important properties include alternative routings, processing time, machine capacity (reliability), machine capability (flexibility), production volume, product demand, and the number of operations done on a machine. The objectives of this paper are to present a review of the literature on similarity coefficients between machines in CMS, to propose a new similarity coefficient between machines incorporating all these important properties of similarity, and to propose a machine cell heuristic approach to group machines into machine cells. An example problem is included and demonstrated in this paper.

Keywords

Main Subjects

References

[1] Aljaber N., Baek W., Chen C.L. (1997), A Tabu search approach to the cell formation
problem; Computers & Industrial Engineering 32; 169-185.
[2] Chang P. T., Lee E.S. (2000), A multisolution method for cell formation-exploring practical
alternatives in group technology manufacturing; Computers and Mathematics with
Applications 40; 1285-1296.
[3] Gunasingh K.R., Lashkari R.S. (1989), Machine grouping problem in cellular manufacturing
systems: An integer programming approach; International Journal of Production Research
27; 1465-1473.
[4] Gupta T. (1991), Clustering algorithms for the design of a cellular manufacturing system-An
analysis of their performance; Computers & Industrial Engineering 20; 461-468.
[5] Gupta T. (1993), Design of manufacturing cells for flexible environmental considering
alternative routing; International Journal of Production Research 31; 1259-1273.
[6] Gupta T., Seifoddini H. (1990), Production data based similarity coefficient for machinecomponent
grouping decisions in the design of a cellular manufacturing system; International
Journal of Production Research 28; 1247-1269.
[7] Islam K.M.S., Sarker B.R. (2000), A similarity coefficient measure and machine-parts
grouping in cellular manufacturing systems; International Journal of Production Research
38; 699-720.
[8] Lee M.K., Luong H.S., Abhary K. (1997), A genetic algorithm based cell design considering
alternative routing; Computer Integrated Manufacturing Systems 10; 93-107.
[9] Leem C.-W., Chen J.J. G. (1996), Fuzzy-set-based machine-cell formation in cellular
manufacturing; Journal of Intelligent Manufacturing 7; 355-364.
[10] Lozano S., Canca D., Guerrero F., Garcia J.M. (2001), Machine grouping using sequence
based similarity coefficients and neural network; Robotics and Computer Integrated
Manufacturing 17; 399-404.
[11] Luong L.H.S. (1993), A cellular similarity coefficient algorithm for the design of
manufacturing cells; International Journal of Production Research 31; 1757-1766.
[12] Luong L.H.S., Kazerooni M., Abhary K. (2001), Genetic algorithms in manufacturing system
design. In Computational Intelligence in Manufacturing Handbook, edited by Jun Wang et
al., Boca Raton, FL (CRC Press LLC).
[13] McAuley J. (1972), Machine grouping for efficient production; The Production Engineer 52;
53-57.
[14] Mosier C. (1989), An experiment investigating the application of clustering procedures and
similarity coefficients to the GT machine cell formation problem; International Journal of
Production Research 27; 1811-1835.
[15] Nair G.J., Narendran T.T. (1998), CASE: A clustering algorithm for cell formation with
sequence data; International Journal of Production Research 36; 157-179.
[16] Nazarlo D., Ramirez B. (2000), Application of mixed integer programming to cellular
manufacturing; Engineering Valuation and Cost Analysis 2; 373-386.
[17] Ponnambalam S.G., Aravindan P. (1994), Design of cellular manufacturing systems using
objective functional clustering algorithms; International Journal of Advanced Manufacturing
Technology 9; 390-397.
[18] Probhakaran G., Janakiraman T.N., Sachithanandam M. (2002), Manufacturing data-based
combined dissimilarity coefficient for machine cell formation; International Journal of
Advanced Manufacturing Technology 19; 889-897.
[19] Ramabhatta V., Nagi R. (1998), An integrated formulation of manufacturing cell formation
with capacity planning and routing; Annals of Operations Research 77; 79-95.
[20] Seifoddini H. (1988), Incorporation of the production volume in machine cells formation in
group technology applications. Recent Developments in Production Research, edited by A.
Mital, (Elsevier Science Publishers B.V., Amsterdam), 562-570.
[21] Seifoddini H. (1989), A probabilistic approach to machine cell formation in group
technology. International Conference of Institute of Industrial Engineers, Toronto, Ontario,
Canada, May 14-17, 625-629.
[22] Seifoddini H., Djassemi M. (1991), The production data-based similarity coefficient versus
Jaccard”s similarity coefficient; Computers & Industrial Engineering 21; 263-266.
[23] Seifoddini H., Djassemi M. (1996), Merits of the production volume based similarity
coefficient in machine cell formation; Journal of Manufacturing Systems 14; 35-44.
[24] Seifoddini H., Wolfe P.M. (1986), Application of the similarity coefficient method in group
technology; IIE Transactions; 271-277.
[25] Seifoddini H., Tjahana B. (1999), Part-family formation for cellular manufacturing: A case
study at Harnischfeger; International Journal of Production Research 37; 3263-3273.
[26] Shaferm S.M., Rogers D.F. (1993), Similarity and distance for cellular manufacturing. Part
II: An extension and comparison; International Journal of Production Research 31; 1315-
1326.
[27] Viswanthan S. (1996), A new approach for solving the P-median problem in group
technology; International Journal of Production Research 34; 2691-2700.
[28] Waghodekar P.H., Sabu S. (1984), Machine-component cell formation in group technology:
MACE; International Journal of Production Research 22; 937-948.
[29] Wilhelm W.E., Chiou C.C., Chang D.B. (1998), Integrating design and planning
considerations in cellular manufacturing; Annals of Operations Research 77; 97-107.
[30] Won Y. (2000), New P-median approach to cell formation with alternative process plans;
International Journal of Production Research 38; 229-240.
[31] Won Y., Kim S. (1997), Multiple criteria clustering algorithm for solving the group
technology problem with multiple process routings; Computers & Industrial Engineering 32;
207-220.
[32] Yasuda K., Yin Y. (2001), A dissimilarity measure for solving the cell formation problem in
cellular manufacturing; Computers & Industrial Engineering 39; 1-17.
[33] Yin Y., Yasuda K. (2002), Manufacturing cells design in consideration of various production
factors; International Journal of Production Research 40; 885-906.
Journal of Industrial and Systems Engineering
Volume 1, Issue 4 - Serial Number 4
January 2008
Pages 318-344

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History

  • Receive Date: 19 January 2007
  • Revise Date: 29 May 2007
  • Accept Date: 14 October 2007

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