Skip to main content
SpringerLink
Log in

Machine–part cell formation through visual decipherable clustering of self-organizing map

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

Machine–part cell formation is used in cellular manufacturing in order to process large varieties, improve quality, and lower work-in-process levels, reducing manufacturing lead time and customer response time while retaining flexibility for new products. This paper presents a new and novel approach for obtaining machine cells and part families. In cellular manufacturing, the fundamental problem is the formation of part families and machine cells. The present paper deals with the self-organizing map (SOM) method, an unsupervised learning algorithm in artificial intelligence which has been used as a visually decipherable clustering tool of machine–part cell formation. The objective of the paper is to cluster the binary machine–part matrix through visually decipherable cluster of SOM color coding and labeling via the SOM map nodes in such a way that the part families are processed in that machine cell. The U-matrix, component plane, principal component projection, scatter plot, and histogram of SOM have been reported in the present work for the successful visualization of the machine–part cell formation. Computational result with the proposed algorithm on a set of group technology problems available in the literature is also presented. The proposed SOM approach produced solutions with a grouping efficacy that is at least as good as any results earlier reported in the literature and improved the grouping efficacy for 70% of the problems and was found to be immensely useful to both industry practitioners and researchers.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Askin RG, Subramanian S (1987) A cost-based heuristic for group technology configuration. Int J Prod Res 25:101–113

    Article  Google Scholar 

  2. Balakrishnan J, Jog PD (1995) Manufacturing cell formation using similarity coefficients and a parallel genetic TSP algorithm formulation and comparison. Math Comput Model 21(12):61–73

    Article  MATH  Google Scholar 

  3. Balasubramanian KN, Panneerselvam R (1993) Covering technique-based algorithm for machine grouping to form manufacturing cells. Int J Prod Res 31(6):1479–1504

    Article  Google Scholar 

  4. Brosse S, Giraudel JL, Lek S (2001) Utilisation of non-supervised neural networks and principle component analysis to study fish assemblages. Ecol Model 146:159–166

    Article  Google Scholar 

  5. Burbidge JL (1979) Group technology in engineering industry. Mechanical Engineering, London

    Google Scholar 

  6. Carpenter GA, Grossberg S (1987) A massively parallel architecture for a self organizing neural pattern recognition machine. Comput Vis Graph Image Process 37:54–115

    Article  Google Scholar 

  7. Carrie S (1973) Numerical taxonomy applied to group technology and plant layout. Int J Prod Res 11:399–416

    Article  Google Scholar 

  8. Chan HM, Milner DA (1982) Direct clustering algorithm for group formation in cellular manufacture. J Manuf Syst 1:65–75

    Article  Google Scholar 

  9. Chandrasekharan MP, Rajagopalan R (1986) MODROC: an extension of rank order clustering for group technology. Int J Prod Res 24(5):1221–1233

    Article  Google Scholar 

  10. Chandrasekharan MP, Rajagopalan R (1989) Groupability: analysis of the properties of binary data matrices for group technology. Int J Prod Res 27(6):1035–1052

    Article  Google Scholar 

  11. Chattopadhyay M, Dan PK, Das N (2009). Application of self organizing map in machine–part cell formation. International Conference on Artificial Intelligence-2009, Tumkur, Bangalore

  12. Chu CH, Tsai M (1990) A comparison of three array-based clustering techniques for manufacturing cell formation. Int J Prod Res 28(8):1417–1433

    Article  Google Scholar 

  13. Flexer A (2001) On the use of self-organising maps for clustering and visualization. Intell Data Anal 5:373–384

    MATH  Google Scholar 

  14. Goncalves JF, Resende MGC (2004) An evolutionary algorithm for manufacturing cell formation. Comp Ind Eng 47:247–273

    Article  Google Scholar 

  15. Guerrero F, Lozano S, Smith KA, Canca D, Kwok T (2002) Manufacturing cell formation using a new self-organizing neural network. Comput Ind Eng 42:377–382

    Article  Google Scholar 

  16. Jain AK, Mao J, Mohiuddin K (1996) Artificial neural networks: a tutorial. IEEE Computer 26(3):31–44

    Google Scholar 

  17. Jeffrey Schaller E (2005) Tabu search procedures for the cell formation problem with intra-cell transfer costs as a function of cell size. Comput Ind Eng 49(3):449–462

    Article  Google Scholar 

  18. Jang J-SR, Sun C-T, Mizutani E (2002) Neuro-fuzzy and soft computing. Prentice Hall, New Delhi

    Google Scholar 

  19. Kao Y, Li YL (2008) Ant colony recognition systems for part clustering problems. Int J Prod Res 46(15):4237–4258

    Article  MATH  Google Scholar 

  20. Kaski S (1997) Data exploration using self-organizing maps. Acta Polytech Scand Math Comput Manag Eng 57(Series No 82):1489–1490

    Google Scholar 

  21. Kaski S, Kohonen T (1998) Tips for processing and color-coding of self-organising maps. In: Deboeck G, Kohonen T (eds) Visual explorations in finance with self-organizing maps. Springer, London, pp 195–202

    Google Scholar 

  22. Kaski S (2001) SOM-based exploratory analysis of gene expression data. In: Allinson N, Yin H, Allinson L, Slack J (eds) Advances in self-organising maps. Springer, London, pp 124–131

    Google Scholar 

  23. Kiang MY (2001) Extending the Kohonen self organising map networks for clustering analysis. Comput Stat Data Anal 38:161–180

    Article  MATH  MathSciNet  Google Scholar 

  24. Kioon SAh, Bulgak AA, Bektas T (2007) Integrated cellular manufacturing systems design with production planning and dynamic system reconfiguration. Eur J Oper Res 192(2):414–428

    Google Scholar 

  25. Kohonen T (1998) Self organising map. Neurocomputing 21:1–6

    Article  MATH  Google Scholar 

  26. Kohonen T (2001) Self organising maps, 3rd edn. Springer, Berlin

    Google Scholar 

  27. Kumar KR, Chandrasekaran MP (1990) Grouping efficacy: a quantitative criterion for goodness of block diagonal forms of binary matrices in group technology. Int J Prod Res 28(2):233–243

    Article  Google Scholar 

  28. Laitinen N, Rantanen J, Laine S, Antikainen O, Rasanen E, Airaksinen S, Ylirussi J (2002) Visualisation of particle size and shape distributions using self-organising maps. Chemometr Intell Lab Syst 62:47–60

    Article  Google Scholar 

  29. Li J, Chu C-H, Wang Y, Yan W (2007) An improved fuzzy clustering method for cellular manufacturing. Int J Prod Res 45(5):1049–1062

    Article  MATH  Google Scholar 

  30. Lin Shih-Wei, Gupta Jatinder ND, Kuo-Ching Y, Zne-Jung L (2008) Using simulated annealing to schedule a flow shop manufacturing cell with sequence-dependent family setup times. Int J Prod Res 47(12):3205–3217

    Article  Google Scholar 

  31. Malave CO, Ramachandran S (1991) Neural network-based design of cellular manufacturing systems. J Intell Manuf 2:305–314

    Article  Google Scholar 

  32. Mancuso S (2001) Clustering of grapevine (Vitis vinifera L.) genotypes with Kohonen neural networks. VITIS 40:59–63

    Google Scholar 

  33. Mosier CT, Taube L (1985) Weighted similarity measure heuristics for the group technology machine clustering problem. Omega 13(6):577–583

    Article  Google Scholar 

  34. Mukattash AM, Adel MB, Tahboub KK (2002) Heuristic approaches for part assignment in cell formation. Comput Ind Eng 42(2–4):329–341

    Article  Google Scholar 

  35. Mukhopadhyay SK, Ramesh BK, Sai K, Vijai V (2009) A note on ‘Modified Hamiltonian chain: a graph theoretic approach to group technology’. Int J Prod Res 47(1):289–298

    Article  Google Scholar 

  36. Murugan M, Selladurai V (2007) Optimization and implementation of cellular manufacturing system in a pump industry using three cell formation algorithms. Int J Adv Manuf Technol 35(1–2):135–149

    Article  Google Scholar 

  37. Pillai V, Madhusudanan, Subbarao Kankata (2008) A robust cellular manufacturing system design for dynamic part population using a genetic algorithm. Int J Prod Res 46(18):5191–5210

    Article  MATH  Google Scholar 

  38. Seifoddini H (1989) Single linkage versus average linkage clustering in machine cells formation applications. Comput Ind Eng 16(3):419–426

    Article  Google Scholar 

  39. Singh N, Rajamani D (1996) Cellular manufacturing systems: design, planning and control. Chapman and Hall, London, pp 73–81

    Google Scholar 

  40. Stanfel L (1985) Machine clustering for economic production. Eng Costs Prod Econ 9:73–78

    Article  Google Scholar 

  41. Suresh NC, Slomp J, Kaparthi S (1999) Sequence-dependent clustering of parts and machines, a fuzzy ART neural network approach. Int J Prod Res 37(12):2793–2816

    Article  MATH  Google Scholar 

  42. Tavakkoli-Moghaddam R, Aryanezhad MB, Safaei N, Vasei M, Azaron A (2007) A new approach for the cellular manufacturing problem in fuzzy dynamic conditions by a genetic algorithm. J Intell Fuzzy Syst 18(4):363–376

    MATH  Google Scholar 

  43. Tay Joc Cing, Ho Nhu Binh (2008) Evolving dispatching rules using genetic programming for solving multi-objective flexible job-shop problems. Comput Ind Eng 54(3):453–473

    Article  Google Scholar 

  44. Venkumar P, Haq AN (2006) Fractional cell formation in group technology using modified ART1 neural networks. Int J Adv Manuf Technol 28(7–8):761–765

    Article  Google Scholar 

  45. Venkumar P, Haq AN (2006) Complete and fractional cell formation using Kohonen self-organizing map networks in a cellular manufacturing system. Int J Prod Res 44(20):4257–4271

    Article  Google Scholar 

  46. Venugopal V, Narendran TT (1994) Machine-cell formation through neural network models. Int J Prod Res 32:2105–2116

    Article  MATH  Google Scholar 

  47. Vesanto J, Himberg J, Alhoniemi E, Parhankangas J (2000) Somtoolbox for matlab 5. Technical Report A57, Helsinki University of Technology, Laboratory of Computer and Information Science, Espoo, Finland

  48. Waghodekar PH, Sahu S (1984) Machine-component cell formation in group technology: MACE. Int J Prod Res 22:937–948

    Article  Google Scholar 

  49. Wang Haibo, Alidaee Bahram, Glover Fred, Kochenberger Gary (2006) Solving group technology problems via clique partitioning. Int J Flex Manuf Syst 18(2):77–97

    Article  MATH  Google Scholar 

  50. Wemmerlov U, Hyer NL (1989) Cellular manufacturing in the US: a survey of users. Int J Prod Res 27:1511–1530

    Article  Google Scholar 

  51. Yang MS, Yang JH (2008) Machine–part cell formation in group technology using a modified ART1 method. Eur J Oper Res 188(1):140–152

    Article  MATH  Google Scholar 

  52. Yong Yin, Kazuhiko Yasuda (2006) Similarity coefficient methods applied to the cell formation problem: a taxonomy and review. Int J Prod Econ 101(2):329–352

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Application, Pailan College of Management and Technology, Kolkata, 700104, India

    Manojit Chattopadhyay & Surajit Chattopadhyay

  2. Industrial Engineering and Management, School of Engineering and Technology, West Bengal University of Technology, Kolkata, 700 064, India

    Pranab K. Dan

Authors
  1. Manojit Chattopadhyay
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Surajit Chattopadhyay
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Pranab K. Dan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Manojit Chattopadhyay.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Chattopadhyay, M., Chattopadhyay, S. & Dan, P.K. Machine–part cell formation through visual decipherable clustering of self-organizing map. Int J Adv Manuf Technol 52, 1019–1030 (2011). https://doi.org/10.1007/s00170-010-2802-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-010-2802-4

Keywords

Search

Navigation