Email this article 
A Comparative Study on Predicting Methods for Cell Formation Problem
(*) 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
The role of Feasibility Assessment (FA) is critical for Cellular Manufacturing (CM) that based on Group Technology (GT) concept, particularly during the design phase. The results may be used in decision making process to decide whether or not job shop may be converted into CM. Through FA some procedures are followed to identify the right predicted cells number that leads to create effective CM. In this article two methods of predicting a number of cells have been examined. The first method is supported by the number of machines in the incidence matrix and the pre-determinable maximum number of machines in each machine cell. The second method was based on Rogers and Tanimoto Similarity Coefficient and Eigenvalues of the Similarity Coefficient Matrix. Ten data sets selected from literatures are used to test these two methods. The results show that the first method is less accurate, depends on trial and error, and has multi values where most of those values are not integers. On the contrary, the second method is very accurate, has only one solution which is an integer value. Therefore it may be considered as a more efficient method. Furthermore, this study suggested an efficient and logical sequence of these two methods in order to identify the performance of the existing methods that are used in solving cell formation problems.A Comparative Study on Predicting Methodsfor Cell Formation Problem
Copyright © 2013 Praise Worthy Prize - All rights reserved.
Keywords
References
S.G. Ponnambalam, R.S. Pandian, S.S. Mohapatra and S. Saravanasankar, Cell formation with workload data in cellular manufacturing system using genetic algorithm, proce. IEEE International Conf. Industrial Engineering and Engineering Management, December 2-4, Singapore, 2007, pp. 674-678.
A. Mukattash, S. Idwan, M.S. Ashhab, M. Samhouri and I.A. Matar, Optimal computerized model for designing cellular manufacturing systems using neural network, J. of Applied sciences, Vol. 11, n. 15, pp. 2837 -2842, 2011.
U. Wemmerlov and N.L. Hyer, Research issues in cellular manufacturing, Int. J. Prod. Res,Vol. 25, pp. 413- 431, 1987.
I. J. Leno, S. Saravanasankar and S.G. Ponnambalam, Integrated layout design approach for cellular manufacturing system environment, J. Applied Sci, Vol. 12, n. 23, pp. 2411-2417, 2012.
J. McAuley, Machine grouping for efficient production, Production Engineer, Vol. 51,n. 2, pp. 53-57, 1972.
C. C. Chang, T.H. Wu and S.H. Chung, A novel approach for cell formation and cell layout design in cellular manufacturing system, Proc. International Conf. on Management and Service Science, September 20-22, Wuhan, China, 2009, pp. 1-4.
T. H. Wu, S.H. Chung and C.C. Chang, Hybrid simulated annealing algorithm with mutation operator to the cell formation problem with alternative process routings, Expert Syst. Appl, Vol. 36, n. 2, pp. 3652-3661, 2009.
K. Zahia and D. Messaoud, A Meta-Heuristics for the Flexible Manufacturing System Problem, (2010) International Review of Mechanical Engineering (IREME), 4 (3), pp. 330-335.
P. Kulandaivelu, S. Sundaram and P.S. Kumar, Neural Network Based Wear Monitoring of Single Point Cutting Tool using Acoustic Emission Techniques, (2011) International Review of Mechanical Engineering (IREME) 5 (1), pp. 52-58.
S. F. Rezeka, B. M. El-Souhily, H. El-Gamal and M. M. Shawkey, Fuzzy Switching of Hybrid Piezoelectric Damping System, (2012) International Review of Mechanical Engineering (IREME) 6 (4), pp. 779-789.
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.
F. Alhourani, and H. Seifoddini, Machine cell formation for production management in cellular manufacturing systems, Int. J. Prod. Res, Vol. 45, pp. 913-934, 2007.
P. K. Arora, A. Haleem and M.K. Singh, Cell formation techniques: A study, Int. J. Eng. Sci. Technol, Vol. 3, pp. 1178-1181, 2011.
F. F. Boctor, A linear formulation of the machine part cell formation problem, International Journal of Production Research, Vol. 29, pp. 343-356, 1991.
Z. Albadawi, H.A. Basher and M. Chen, A mathematical approach for the formation of manufacturing cells, Comput. Ind. Eng, Vol. 48, pp. 3-21, 2005.
Y. Yin, and K. Yasuda Similarity coefficient methods applied to the cell formation problem: a comparative investigation, Computers and Industrial Engineering, Vol. 48, pp. 471-489, 2005.
G. Jeon, H. R. Leep and H.R. Parsaei, A cellular manufacturing system based on new similarity coefficient which considers alternative routes during machine failure, Comput Ind. Eng, Vol. 34, pp. 21-36, 1998.
B. R. Sarker, and K.M.S. Islam, Relative performances of similarity and dissimilarity measures, Comput. Ind. Eng, Vol. 37, pp.769-807, 1999.
B. R. Sarker, and K.M.S. Islam, A similarity coefficient measure and machine-parts grouping in cellular manufacturing systems, Int. J Prod. Res, Vol. 38, n. 3, pp. 699-720, 2000.
K. Yasuda, and Y. Yin, A dissimilarity measure for solving the cell formation problem in cellular manufacturing, Comput. Ind. Eng, Vol. 39, pp. 1-17, 2001.
U. Wemmerlov, and N. L. Hyer, Cellular manufacturing in the U.S. industry: A survey of users, Int. J. Prod. Res, Vol. 27, pp. 1511-1530, 1989.
L. Luong, J. He, K. Abhary and L. Qiu, A decision support system for cellular manufacturing system design, Comput. Ind. Eng, Vol. 42, pp. 457-70, 2002.
R. A. Maleki, Flexible Manufacturing Systems: The Technology and Management (Prentice-Hall, 1991).
G. H. Dunteman, Principal Components Analysis (2ndEdn, Sage Publication, Newbury Park, CA, USA, 1989).
H. A. Bashir, and S. Karaa, Assessment of clustering tendency for the design of cellular manufacturing systems, J. Manuf. Technol. Manage, Vol. 19, pp. 1004-1014, 2008.
B. Arvindh, and S.A. Irani, Principal component analysis for evaluating the feasibility of cellular manufacturing without initial machine-part matrix clustering, Int. J. Prod. Res,Vol. 32, pp. 1909-1938, 1994.
Burbidge, J.L., 1992. Change to group technology: Process organization is obsolete. Int. J. Prod. Res, Vol. 30, pp. 1209-1219.
A. M. Mukattash, and M. D. Al-Tahat, A developed mathematical computerized approach for designing cellular manufacturing systems, J. of applied sciences, Vol. 6, n. 9, pp. 1915-1923, 2006.
I. H. Garbie, H.R. Parsaei and H.R. Leep, Machine cell formation based on a new similarity coefficient, J. Ind. Syst. Eng, Vol. 1, pp. 318-344, 2008.
W. E. Wilhelm, C.C. Chiou and D.B. Chang, Integrating design and planning considerations in cellular manufacturing, Ann. Oper. Res, Vol. 77, pp. 97-107, 1998.
V. Ramabhatta, and R. Nagi, An integrated formulation of manufacturing cell formation with capacity planning and multiple routings, Ann. Oper. Res, Vol. 77, pp. 79-95, 1998.
J. S. Rogers, and T.T. Tanimoto, A computer program for classifying plants, Science, Vol. 132, pp. 1115-1118, 1960.
N. Singh, and D. Rajamani, Cellular Manufacturing Systems: Design, Planning and Control (Chapman and Hall, London, UK, 1996).
P. H. Waghodekar, and S. Sahu, Machine-component cell formation in group technology: MACE, Int. J. Prod. Res, Vol. 22, pp. 937-948, 1984.
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.
M. Chattopadhyay, S. Chattopadhyay and P. K. Dan, Machine-part cell formation through visual decipherable clustering of self-organizing map, Int. J. Adv. Manuf. Technol, Vol. 52, pp. 1019-1030, 2011.
H. F. Kaiser, The application of electronic computers to factor analysis, Educational and psychological Measurement, Vol. 20, pp. 141-151, 1960.
Refbacks
- There are currently no refbacks.
Please send any question about this web site to info@praiseworthyprize.com
Copyright © 2005-2024 Praise Worthy Prize
