Evaluating the performance of the Similarity Coefficient Measures in the Feasibility Assessment of Cellular Manufacturing

Sanaa Ali Hamza(1), Erry Yulian Adesta(2*)

(1) Al-Furat Al-Awsat Technical University, Iraq
(2) Department of Manufacturing and Materials Engineering, Faculty of Engineering, International Islamic University Malaysia, Malaysia
(*) Corresponding author


DOI's assignment:
the author of the article can submit here a request for assignment of a DOI number to this resource!
Cost of the service: euros 10,00 (for a DOI)

Abstract


Cellular Manufacturing (CM) system is a modern and effective manufacturing system that considered as a best application of Group Technology (GT) principles. To apply CM in the real life, three issues should be counted (i) Feasibility Assessment (FA) (ii) design of CM (iii) application processes. FA is an important issue to evaluate the data of the job shop system and predict the number of machine cells before the designing and applying of CM. In the previous literature, there are a large number of Similarity Coefficients (SCs) have been proposed to solve the Cell Formation Problem (CFP). These SCs have different accuracy and the comparative studies that done to identify the performance of these SCs are very limited and focusing only on the design issue of CM. Also in the past literature, a comparative study on evaluating the performance of different SCs in the FA is overlooked. The novelty of the present study is to identify the performance of different SCs in the FA of CM. In this paper, 20 General Purposes Similarity Coefficients (GPSCs) and problem oriented SCs have been evaluated in the FA. A set of thirty test problems selected from literature are examined in this study. The differences in the number of machine cells that produced by using each SC comparing with the majority of the SCs are identified and used as evaluation criteria. The results show that the GPSCs and problem oriented SCs can be classified into two classes: the first class considered as an efficient GPSCs which include: Sorenson, Rusell and Rao, Ochiai, Dot-product, However the second class is inefficient which consists four GPSCs: Sokal and Sneath 4, Yule, Hamann and Phi.
Copyright © 2014 Praise Worthy Prize - All rights reserved.

Keywords


Cellular Manufacturing; Feasibility Assessment; Cell Formation; General Purpose Similarity Coefficients

Full Text:

PDF


References


Y. Kao, and Fu. SC, An ant-based clustering algorithm for manufacturing cell design, Int J Adv Manuf Technol, Vol. 28, PP. 1182-1189, 2006.
http://dx.doi.org/10.1007/s00170-004-2475-y

J. McAuley, Machine grouping for efficient production, Production Engineer, Vol. 52, PP. 53–57, 1972.
http://dx.doi.org/10.1049/tpe.1972.0006

P.Kulandaivelu, S.Sundaram, P.Senthil Kumar, Neural Network Based Wear Monitoring of Single Point Cutting Tool using Acoustic Emission Techniques, I.RE.M.E, Vol. 5, n. 1, pp. 52-58, 2011.
http://dx.doi.org/10.1007/s12046-013-0130-8

Kh. Zahia, and D. Messaoud, A Meta-Heuristics for the Flexible Manufacturing System Problem, I.RE.M.E , Vol. 4, n. 3, pp. 330-335, 2010.

M. Kahrom , S. M. Javadi , and P. Haghparast, Application of Multi Objective Genetic Algorithm to Optimize Heat Transfer Enhancement from a Flat Plate, I.RE.M.E, Vol. 4, n. 2, pp. 167-175, 2010.
http://dx.doi.org/10.5829/idosi.ije.2012.25.01c.08

P.K. Arora, A. Haleem, M.K. Singh, Cell Formation Techniques –A Study, international Journal of Engineering Science and Technology, Vol. 3, n. 2, PP. 1178-1181, 2011.

U. Wemmerlov and N. Hyer, Research issues in cellular manufacturing, International Journal of Production Research, Vol. 25, PP. 413– 431, 1987.
http://dx.doi.org/10.1080/00207548708919850

Y. Yin, and K. Yasuda, Similarity coefficient methods applied to the cell formation problem: a comparative investigation. Computers & Industrial Engineering, Vol. 48, PP. 471–489, 2005.
http://dx.doi.org/10.1016/j.cie.2003.01.001

S. Lozano, D. Canca, F. Guerrero and J.M. Garcia, Machine grouping using sequence based similarity coefficients and neural network, Robotics and Computer Integrated Manufacturing, Vol. 17, PP. 399-404, 2001.
http://dx.doi.org/10.1016/s0736-5845(01)00015-1

N.F. Samatova, T.E. Potok and M.R. Leuze, Vector space model for the generalized parts grouping problem. Robot Comput. Integr. Manuf, Vol. 17, PP. 73–80, 2001.
http://dx.doi.org/10.1016/s0736-5845(00)00039-9

G. Prabhakaran, T.N. Janakiraman and M. Sachithanandam, Manufacturing data based combined dissimilarity coefficient for machine cell formation. International Journal of Advanced Manufacturing Technology, Vol. 19, PP. 889–897, 2002.
http://dx.doi.org/10.1007/s001700200101

B.R. Sarker and K.M.S. Islam KMS, Relative performances of similarity and dissimilarity measures, Comput. Ind. Eng Vol. 37, PP. 769–807, 1999.
http://dx.doi.org/10.1016/s0360-8352(00)00011-5

Y. Yin, and K. Yasuda, Similarity coefficient methods applied to the cell formation problem: A taxonomy and review, International Journal of Production Economics, Vol. 101, n. 2, PP. 329-352, 2006.
http://dx.doi.org/10.1016/j.ijpe.2005.01.014

S.G. Ponnambalam, R. Sudhakara Pandian, S.S. Mohapatra and S. Saravanasankar, Cell formation with workload data in cellular manufacturing system using genetic algorithm, International Conference on IEEM-IEEE, PP. 674-678, 2007. 2-4 Dec, Monash Univ, Petaling Jaya.

J.L. Burbidge, Change to group technology: process organization is obsolete. Int. J. of Production Research, Vol. 30, PP. 1209-1219, 1992.
http://dx.doi.org/10.1080/00207549208942951

S.M. Shafer and D.F. Rogers, Similarity and distance measures for cellular manufacturing, part I: a survey, Int J Prod Res, Vol. 31, n. 5, pp.1133–1142, 1993.
http://dx.doi.org/10.1080/00207549308956779

C.T. Mosier, J. Yelle and G. Walker, Survey of similarity coefficient based methods as applied to the group technology configuration problem, Omega, The International Journal of Management Science, Vol. 25, pp. 65-79, 1997.
http://dx.doi.org/10.1016/s0305-0483(96)00045-x

H.A. Basher and S. Karaa, Assessment of clustering tendency for the design of cellular manufacturing systems, Journal of Manufacturing Technology Management, Vol. 19, n. 8, pp. 1004-1014, 2008.
http://dx.doi.org/10.1108/17410380810911754

N. Singh and D. Rajamani, Cellular manufacturing systems: Design, planning and control, Chapman & Hall, London, 1996.
http://dx.doi.org/10.1080/07408179808966442

P.H. Waghodekar and S. Sahu, Machine-component cell formation in group technology: MACE, International Journal of Production Research, Vol. 22, n. 6, pp. 937–948, 1984.
http://dx.doi.org/10.1080/00207548408942513

J.R. King and V. Nakornchai, Machine component group formation in group technology: review and extension, Int J Prod Res, Vol. 20, pp.117–133, 1982.
http://dx.doi.org/10.1080/00207548208947754

P. Arikaran and V. Jayabalan, A Grouping Genetic Algorithm for Solving the Machine Component Grouping Problem, European Journal of Scientific Research, Vol. 63, n. 3, pp. 347-357, 2011.

M. Murugan and V. Selladurai, Formation of Machine Cells/ Part Families in Cellular Manufacturing Systems Using an ART-Modified Single Linkage Clustering Approach – A Comparative Study, Jordan Journal of Mechanical and Industrial Engineering, Vol. 5, n. 3, pp. 199-212, 2011.

D.S. Chen, H.C. Chen and J.M. Part, An improved ART neural net for machine cell formation, Journal of Materials Processing Technology, Vol. 61, pp. 1–6, 1996.
http://dx.doi.org/10.1016/0924-0136(96)02457-0

F.F. Boctor, A linear formulation of the machine-part cell formation problem, International Journal of Production Research, Vol. 29, pp. 343-356, 1991.
http://dx.doi.org/10.1080/00207549108930075

A. Kusiak, The generalized group technology concept, Int J Prod Res Vol. 25, pp. 561–569, 1987.
http://dx.doi.org/10.1080/00207548708919861

I. Mahdavi, M.M. Paydar, M. Solimanpur and M. Saidi-Mehrabad, Amethematical model for integrating cell formation problem with machine layout, International Journal of Industrial Engineering & Production Research, Vol. 21, n. 2, pp. 61-70, 2010.

M.P. Chandrasekharan and R. Rajagopalan, An ideal seed non-hierarchical clustering algorithm for cellular manufacturing, International Journal of Production Research, Vol. 24, pp. 451–464, 1986.
http://dx.doi.org/10.1080/00207548608919741

M. Salehi and R.T. Moghaddam, A grouping genetic algorithm for the cell formation problem. International Journal of Natural and Engineering Sciences, Vol. 3, n. 1, pp. 73-78, 2009.

M. Chattopadhyay, S. Chattopadhyay and P.K. Dan, Machine-Part cell formation through visual decipherable clustering of Self Organizing Map, International Journal of Advanced Manufacturing Technology, Vol. 52, n. 9-12, pp. 1019-1030, 2011.
http://dx.doi.org/10.1007/s00170-010-2802-4

C.T. Mosier and L. Taube, Weighted similarity measure heuristics for the group technology machine clustering problem, Omega, Vol. 13, n. 6, pp. 577–583, 1985.
http://dx.doi.org/10.1016/0305-0483(85)90046-5

H.M. Chan and D.A. Milner, Direct clustering algorithm for group formation in cellular manufacture, Journal of Manufacturing Systems, Vol. 1, n. 1, pp. 65-74, 1982.
http://dx.doi.org/10.1016/s0278-6125(82)80068-x

J.R. King, Machine-component grouping in production flow analysis: an approach using a rank order clustering algorithm, Int J Prod Res, Vol. 18, n. 2, pp. 213–232, 1980.
http://dx.doi.org/10.1080/00207548008919662

Z. Albadawi, H.A. Bashir and M. Chen, A mathematical approach for the formation of cells,Computers and Industrial Engineering Vol. 48, pp. 3-21, 2005.
http://dx.doi.org/10.1016/j.cie.2004.06.008

H.F. Kaiser, The application of electronic computers to factor analysis. Educational and psychological Measurement, Vol. 20, pp. 141-51, 1960.
http://dx.doi.org/10.1177/001316446002000116

J. S. Rogers, and T.T. Tanimoto, A computer program for classifying plants, Science, Vol. 132, pp. 1115-1118, 1960.
http://dx.doi.org/10.1126/science.132.3434.1115


Refbacks

  • There are currently no refbacks.



Please send any question about this web site to info@praiseworthyprize.com
Copyright © 2005-2019 Praise Worthy Prize