Year 2017, Volume 21 , Issue 5, Pages 1031 - 1044 2017-10-01

Performance comparison of different clustering methods for manufacturing cell formation
İmalat hücresi oluşturulması için farklı kümeleme yöntemlerinin performans karşılaştırması

Sinem Büyüksaatçı Kiriş [1] , Fatih Tüysüz [2]


This study refers to cell formation, which is the fundamental and important stage of cellular manufacturing system design. Three widely used methods that are K-means clustering algorithm, average-linkage clustering algorithm and fuzzy clustering using expectation maximization algorithm for cell formation problem are studied. A real life application of these methods for the design of cylinder department of a construction equipment manufacturer is performed. The performance of each applied algorithm is evaluated according to intracellular voids, intracellular processing intensity and intercellular transportation measures. The application results indicate that K-means clustering algorithm, which is the most widely applied and most known one of classical clustering algorithms, is still an effective method for cell formation.

Bu çalışma, hücresel imalat sistemi tasarımının temel ve önemli aşaması olan hücre oluşturmaya değinmektedir. Çalışmada hücre oluşturma uygulamalarında yaygın olarak kullanılan üç yöntem; k-ortalamalar kümeleme algoritması, ortalama bağlantılı kümeleme algoritması ve beklenti maksimizasyonu algoritmasını kullanan bulanık kümeleme algoritması incelenmektedir. Bir inşaat ekipmanı üreticisinin silindir bölümünün tasarımı için bu yöntemlerin gerçek hayat uygulaması gerçekleştirilmiştir. Uygulanan her algoritmanın performansı hücre içi boşluklar, hücre içi işlem yoğunluğu ve hücreler arası taşıma miktarı ölçütlerine göre değerlendirilmektedir. Uygulama sonuçları, klasik kümeleme algoritmalarından en çok bilinen ve en yaygın olarak uygulanan k-ortalamalar kümeleme algoritmasının hücre oluşturma için hala etkili bir yöntem olduğunu göstermektedir.

  • S. P. Mitrofanov, The scientific principles of group technology. Boston Spa, Yorks, UK: National Lending Library Translation, 1966.
  • R. G. Askin and C. R. Standridge, C. R., Modeling and analysis of manufacturing systems. John Wiley & Sons Inc, 1993.
  • B. Bootaki, I. Mahdavi and M. M. Paydar, “New criteria for configuration of cellular manufacturing considering product mix variation,” Computers & Industrial Engineering, vol. 98, pp. 413-426, August 2016.
  • M. Mohammadi and K. Forghani, “A hybrid method based on genetic algorithm and dynamic programming for solving a bi-objective cell formation problem considering alternative process routings and machine duplication,” Applied Soft Computing, vol. 53, pp. 97-110, April 2017.
  • I. Mahdavi, E. Teymourian, N. T. Baher and V. Kayvanfar, “An integrated model for solving cell formation and cell layout problem simultaneously considering new situations,” Journal of Manufacturing Systems, vol. 32, no. 4, pp. 655-663, October 2013.
  • G. Papaioannou, J. M. Wilson, “The evolution of cell formation problem methodologies based on recent studies (1997–2008): Review and directions for future research,” European journal of operational research, vol. 206, no. 3, pp. 509-521, November 2010.
  • V. Modrák, P. Semančo, “Developments in Modern Operations Management and Cellular Manufacturing,” in Operations Management Research and Cellular Manufacturing Systems: Innovative Methods and Approaches: Innovative Methods and Approaches, V. Modrák, Ed., IGI Global, 2011.
  • A. I. Shahrukh, Handbook of cellular manufacturing systems. New York, John Wiley & Sons, 1999.
  • N. Singh and D. Rajamani, D., Cellular manufacturing systems: design, planning and control. Springer Science & Business Media, 2012.
  • H. M. Selim, R. G. Askin and A. J. Vakharia, “Cell formation in group technology: review, evaluation and directions for future research,” Computers & Industrial Engineering, vol. 34, no. 1, pp. 3-20, January 1998.
  • U. Wemmerlöv and N. L. Hyer, “Procedures for the part family/machine group identification problem in cellular manufacturing,” Journal of Operations Management, vol. 6, no. 2, pp. 125-147, February 1986.
  • S. S. Heragu, “Group technology and cellular manufacturing,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 24, no. 2, pp. 203-215, February 1994.
  • S. S. Heragu and Y. P. Gupta, “A heuristic for designing cellular manufacturing facilities,” The International Journal of Production Research, vol. 32, no. 1, pp. 125-140, 1994.
  • J. R. King and V. Nakornchai, “Machine-component group formation in group technology: review and extension,” The International Journal of Production Research, vol. 20, no. 2, pp. 117-133, 1982.
  • B. Adenso-Dı́az, S. Lozano, J. Racero and F. Guerrero, “Machine cell formation in generalized group technology,” Computers & Industrial Engineering, vol. 41, no. 2, pp. 227-240, November 2001.
  • C. T. Mosier, “An experiment investigating the application of clustering procedures and similarity coefficients to the GT machine cell formation problem,” The International Journal Of Production Research, vol. 27, no. 10, pp.1811-1835, 1989.
  • S. M. Shafer and J. R. Meredith, “A comparison of selected manufacturing cell formation techniques,” The International Journal of Production Research, vol. 28, no. 4, pp.661-673, 1990.
  • C. H. Chu and M. Tsai, “A comparison of three array-based clustering techniques for manufacturing cell formation,” The International Journal Of Production Research, vol. 28, no. 8, pp.1417-1433, 1990.
  • J. S. Morris and R. J. Tersine, “A simulation analysis of factors influencing the attractiveness of group technology cellular layouts,” Management Science, vol. 36, no. 12, pp. 1567-1578, 1990.
  • J. Miltenburg and W. Zhang, “A comparative evaluation of nine well-known algorithms for solving the cell formation problem in group technology,” Journal of operations management, vol. 10, no. 1, pp. 44-72, 1991.
  • A. G. Burgess, I. Morgan and T. E. Vollmann, “Cellular manufacturing: its impact on the total factory,” The International Journal of Production Research, vol. 31, no. 9, pp. 2059-2077, 1993.
  • D. F. Rogers and S. M. Shafer, “Measuring cellular manufacturing performance,” in Planning, Design and Analysis of Cellular Manufacturing Systems, A.K. Kamrani, H.R. Parsaei and D.H. Liles Ed., Elsevier Science, B.V., pp.147-165, 1995.
  • B. R. Sarker, “Measures of grouping efficiency in cellular manufacturing systems,” European Journal of Operational Research, vol. 130, no. 3, pp. 588-611, May 2001.
  • K. B. Keeling, E. C. Brown and T. L. James, “Grouping efficiency measures and their impact on factory measures for the machine-part cell formation problem: A simulation study,” Engineering Applications of Artificial Intelligence, vol. 20, no. 1, pp. 63-78, February 2007.
  • H. C. Babacan, “Çok Amaçlı Hücresel İmalat Tasarımı ve Hidromek Silindir Üretim Tesisinde Bir Uygulama,” Graduate Thesis, Gazi University, Ankara, Turkey, 2008.
  • J. MacQueen, “Some methods for classification and analysis of multivariate observations,” In Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, vol. 1, no. 14, pp. 281-297, 1967.
  • A. K. Jain, “Data clustering: 50 years beyond K-means,” Pattern recognition letters, vol. 31, no. 8, pp. 651-666, 2010.
  • R. Real and J. M. Vargas, “The probabilistic basis of Jaccard's index of similarity,” Systematic biology, vol. 45, no. 3, pp. 380-385, 1996.
  • Y. Yin and K. Yasuda, “Similarity coefficient methods applied to the cell formation problem: a comparative investigation,” Computers & industrial engineering, vol. 48, no. 3, pp. 471-489, May 2005.
  • A. P. Dempster, N. M. Laird and D. B. Rubin, “Maximum likelihood from incomplete data via the EM algorithm,” Journal of the Royal Statistical Society. Series B (methodological), vol. 39, no. 1, pp. 1-38, 1977.
  • F. Dellaert, The expectation maximization algorithm. Georgia Institute of Technology, 2002.
  • J. Han, M. Kamber and J. Pei, Data mining: concepts and techniques. 3rd Edition, Elsevier, 2012.
  • J. R. King, “Machine-component grouping in production flow analysis: an approach using a rank order clustering algorithm,” International Journal of Production Research, vol. 18, no. 2, pp. 213-232, 1980.
Subjects Industrial Engineering
Published Date Ekim 2017
Journal Section Research Articles
Authors

Author: Sinem Büyüksaatçı Kiriş
Institution: Istanbul University
Country: Turkey


Author: Fatih Tüysüz
Institution: Istanbul University
Country: Turkey


Dates

Application Date : May 3, 2017
Acceptance Date : August 14, 2017
Publication Date : October 1, 2017

Bibtex @research article { saufenbilder310267, journal = {Sakarya University Journal of Science}, issn = {1301-4048}, eissn = {2147-835X}, address = {}, publisher = {Sakarya University}, year = {2017}, volume = {21}, pages = {1031 - 1044}, doi = {10.16984/saufenbilder.310267}, title = {Performance comparison of different clustering methods for manufacturing cell formation}, key = {cite}, author = {Büyüksaatçı Kiriş, Sinem and Tüysüz, Fatih} }
APA Büyüksaatçı Kiriş, S , Tüysüz, F . (2017). Performance comparison of different clustering methods for manufacturing cell formation. Sakarya University Journal of Science , 21 (5) , 1031-1044 . DOI: 10.16984/saufenbilder.310267
MLA Büyüksaatçı Kiriş, S , Tüysüz, F . "Performance comparison of different clustering methods for manufacturing cell formation". Sakarya University Journal of Science 21 (2017 ): 1031-1044 <http://www.saujs.sakarya.edu.tr/en/issue/26998/310267>
Chicago Büyüksaatçı Kiriş, S , Tüysüz, F . "Performance comparison of different clustering methods for manufacturing cell formation". Sakarya University Journal of Science 21 (2017 ): 1031-1044
RIS TY - JOUR T1 - Performance comparison of different clustering methods for manufacturing cell formation AU - Sinem Büyüksaatçı Kiriş , Fatih Tüysüz Y1 - 2017 PY - 2017 N1 - doi: 10.16984/saufenbilder.310267 DO - 10.16984/saufenbilder.310267 T2 - Sakarya University Journal of Science JF - Journal JO - JOR SP - 1031 EP - 1044 VL - 21 IS - 5 SN - 1301-4048-2147-835X M3 - doi: 10.16984/saufenbilder.310267 UR - https://doi.org/10.16984/saufenbilder.310267 Y2 - 2017 ER -
EndNote %0 Sakarya Üniversitesi Fen Bilimleri Enstitüsü Dergisi Performance comparison of different clustering methods for manufacturing cell formation %A Sinem Büyüksaatçı Kiriş , Fatih Tüysüz %T Performance comparison of different clustering methods for manufacturing cell formation %D 2017 %J Sakarya University Journal of Science %P 1301-4048-2147-835X %V 21 %N 5 %R doi: 10.16984/saufenbilder.310267 %U 10.16984/saufenbilder.310267
ISNAD Büyüksaatçı Kiriş, Sinem , Tüysüz, Fatih . "Performance comparison of different clustering methods for manufacturing cell formation". Sakarya University Journal of Science 21 / 5 (October 2017): 1031-1044 . https://doi.org/10.16984/saufenbilder.310267
AMA Büyüksaatçı Kiriş S , Tüysüz F . Performance comparison of different clustering methods for manufacturing cell formation. SAUJS. 2017; 21(5): 1031-1044.
Vancouver Büyüksaatçı Kiriş S , Tüysüz F . Performance comparison of different clustering methods for manufacturing cell formation. Sakarya University Journal of Science. 2017; 21(5): 1044-1031.