Optimization and implementation of cellular manufacturing system in a pump industry using three cell formation algorithms

ORIGINAL ARTICLE

Abstract

In recent years cellular manufacturing has become an effective tool for improving productivity. Attainment of full benefits of cellular manufacturing depends firstly on the design of the machine cells and part families and secondly on the method of operation which take full advantages of cell properties. Inappropriate methods of loading and scheduling can even lead to the failure of cellular manufacturing systems (CMS), however efficiently the cell is designed. This paper examines three array-based clustering algorithms, namely rank order clustering (ROC), rank order clustering-2 (ROC2) and direct clustering analysis (DCA) for manufacturing cell formation, with a real-life example to demonstrate the effectiveness of various clustering algorithms. The machine cell formation methods considered in this comparative and evaluative study belongs to the cluster formation approach of solving the MCF problem. The most effective method is selected and used to build the cellular manufacturing system. The comparison and evaluation are performed using four published performance measures and compares the improvements with the existing conventional system and the cellular manufacturing system. The above algorithms were written in the C++ language on an Intel/Pentium III-PC-compatible system.

Keywords

Group technology (GT) Machine cells Cellular manufacturing system (CMS) Machine cell formation (MCF) Part family 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Akturk MS, Turkcan A (2000) Cellular manufacturing system design using a holonistic approach. Int J Prod Res 38(10):2327–2347MATHCrossRefGoogle Scholar
  2. 2.
    Anderberg MR (1973) Cluster analysis for applications. Academic Press, New YorkMATHGoogle Scholar
  3. 3.
    Burbridge JL (1971) Production flow analysis. Prod Eng 50:139–152CrossRefGoogle Scholar
  4. 4.
    Ballakur A, Steudel HJ (1987) A within-cell utilization based heuristic for designing cellular manufacturing systems. Int J Prod Res 25:639–665CrossRefGoogle Scholar
  5. 5.
    Carrie AS (1973) Numerical taxonomy applied to group technology and plant layout. Int J Prod Res 11:399–416CrossRefGoogle Scholar
  6. 6.
    Chan FTS, Abhary K (1996) Design and evaluation of automated cellular manufacturing systems with simulation modelling and AHP approach: a case study. Int J Manuf Technol Manag, Integr Manuf Syst 7(6):39Google Scholar
  7. 7.
    Chan HM, Milner DA (1982) Direct clustering algorithm for group formation in cellular manufacture. J Manuf Syst 1:65–75Google Scholar
  8. 8.
    Chan FTS, Lau KW, Chan PLY (2004) A holistic approach to manufacturing cell formation: incorporation of machine flexibility and machine aggregation. Proc Inst Mech Eng B J Eng Manuf 218(10):1279–1296Google Scholar
  9. 9.
    Chan FTS, Lau KW, Chan PLY, Choy KL (2006) Two-stage approach for machine-part grouping and cell layout problems. Int J Rob Comput Integr Manuf 22(3):217–238CrossRefGoogle Scholar
  10. 10.
    Chandrasekharan MP, Rajagopalan R (1986a) An ideal seed non-hierarchical clustering algorithm for cellular manufacturing. Int J Prod Res 24:451–464MATHCrossRefGoogle Scholar
  11. 11.
    Chandrasekharan MP, Rajagopalan R (1986b) MODROC: an extension of rank order clustering for group technology. Int J Prod Res 24:1221–1233CrossRefGoogle Scholar
  12. 12.
    Cheng CH, Kumar A, Motwani J (1995) A comparative examination of selected cellular manufacturing clustering algorithms. Int J Oper Prod Manage 15(12):86–97CrossRefGoogle Scholar
  13. 13.
    Chu CH (1989) Clustering analysis in manufacturing cellular formation. OMEGA: Int J Manag Sci 17:289–295CrossRefGoogle Scholar
  14. 14.
    Chu CH, Tsai M (1990) A comparison of three array-based clustering techniques for manufacturing cell formation. Int J Prod Res 28(8):1417–1433CrossRefGoogle Scholar
  15. 15.
    Cunningham KM, Ogilvie JC (1971) Evaluation of hierarchical grouping techniques: a preliminary study. Comput J 15:209–213CrossRefGoogle Scholar
  16. 16.
    Dimopoulos C, Mort N (2001) A hierarchical clustering methodology based on genetic programming for the solution of simple cell-formation problems. Int J Prod Res 39(1):185–198Google Scholar
  17. 17.
    Gongaware TA, Ham I (1981) Cluster analysis applications for group technology-manufacturing systems. Manuf Eng Trans 503–508Google Scholar
  18. 18.
    Harhalakis G, Nagi R, Proth JM (1990) An efficient heuristic in manufacturing cell formation for group technology applications. Int J Prod Res 28(1):185–198CrossRefGoogle Scholar
  19. 19.
    Hassan M, Selim, Reda MS, Abdel AAL, Aby I, Mahdi (2003) Formation of machine groups and part families: a modified SLC method and comparative study. J Integr Manuf Syst 123–137Google Scholar
  20. 20.
    Hsu CP (1990) Similarity coefficient approaches to machine-component cell formation in cellular manufacturing: a comparative study. PhD thesis, Industrial and Systems Engineering, University of Wisconsin-MilwaukeeGoogle Scholar
  21. 21.
    Jacobs FR (1985) Computerized production flow analysis. In: Whitehouse GE (ed) Soft cover software: 28 microcomputer programs for IEs and manager. IE and Management Press, Georgia, pp 61–67Google Scholar
  22. 22.
    Kaparthi S, Suresh NC (1994) Performance of selected part-machine grouping techniques for data sets of wide ranging sizes and imperfection. Decis Sci 25(4):515–539CrossRefGoogle Scholar
  23. 23.
    Khator SK, Irani SA (1987) Cell formation in group technology: a new approach. Comput Ind Eng 12:131–142CrossRefGoogle Scholar
  24. 24.
    King JR (1980a) Machine-component group formation in-group technology. OMEGA 8:193–199CrossRefGoogle Scholar
  25. 25.
    King JR (1980b) Machine-component grouping in production flow analysis: an approach using a rank order-clustering algorithm. Int J Prod Res 18:213–232CrossRefGoogle Scholar
  26. 26.
    King JR, Nakornchai V (1982) Machine-component group formation in-group technology: review and extension. Int J Prod Res 20:117–133CrossRefGoogle Scholar
  27. 27.
    Kuiper FK, Fisher L (1975) A Monte Carlo comparison of six clustering procedures. Biometrics 777–783Google Scholar
  28. 28.
    Kusiak A (1987) The generalized group technology concept. Int J Prod Res 25:561–569CrossRefGoogle Scholar
  29. 29.
    Kusiak A, Chow WS (1987) Efficient solving of the group technology problem. J Manuf Syst 6:117–124CrossRefGoogle Scholar
  30. 30.
    Mansouri SA, Moattar-Husseini SM, Zegordi SH (2003) A genetic algorithm for multiple objective dealing with exceptional elements in cellular manufacturing. Prod Plan Control 14(5):437–446CrossRefGoogle Scholar
  31. 31.
    McAuley J (1972) Machine grouping for efficient production. Prod Eng 53–57Google Scholar
  32. 32.
    McCormick JR, WT, Scmwitzer PJ, White TW (1972) Problem decomposition and data reorganization by a clustering technique. Oper Res 20:993–1009MATHCrossRefGoogle Scholar
  33. 33.
    Miltenburg J, Zhang W (1991) A comparative evaluation of nine well-known algorithms for solving the cell formation problem in group technology. J Oper Manag 10:44–72CrossRefGoogle Scholar
  34. 34.
    Mosier CT, Taube L (1985) Weighted similarity coefficient heuristics for the group technology machine clustering problem. Int J Manag Sci 13(6):577–583Google Scholar
  35. 35.
    Mosier CT (1989) An experiment investigating the application of clustering procedures and similarity coefficients to the GT machine cell formation problem. Int J Prod Res 27:1811–1835CrossRefGoogle Scholar
  36. 36.
    Nair GJ, Narendran TT (1996) Grouping index: a new quantitative criterion for goodness of block-diagonal forms in-group technology. Int J Prod Res 34:2767–2782MATHCrossRefGoogle Scholar
  37. 37.
    Purcheck GFK (1974) Combinatorial groupings lattice-theoretical method for the design of manufacturing systems. J Cybern 4:27–60Google Scholar
  38. 38.
    Purcheck GFK (1975) A mathematical classification as a basis for the design of group technology production cells. Prod Eng 35–48Google Scholar
  39. 39.
    Rajagopalan R, Batra JL (1975) Design of cellular production systems: a graph theoretic approach. Int J Prod Res 13:567–579CrossRefGoogle Scholar
  40. 40.
    Rand WM (1977) Objective criteria for the evaluation of clustering methods. J Am Stat Assoc 66:846–850CrossRefGoogle Scholar
  41. 41.
    Sarker BR, Mondal S (1999) Grouping efficiency measures in cellular manufacturing: a survey and critical review. Int J Prod Res 37:285–314MATHCrossRefGoogle Scholar
  42. 42.
    Sarker BP, Khan MA (2001) Comparison of existing grouping efficiency measures and a new weighted efficiency measure. IIE Trans 33:11–27Google Scholar
  43. 43.
    Seifoddini H, Djassemi M (1996) A new grouping measure for evaluation of machine-component matrices. Int J Prod Res 34:1179–1193MATHCrossRefGoogle Scholar
  44. 44.
    Stanfel LE (1985) Machine clustering for economic production. Eng Costs Prod Econ 9:73–81CrossRefGoogle Scholar
  45. 45.
    Tarsuslugil M, Bloor J (1979) The use of similarity coefficients and cluster analysis in production flow analysis. Proceedings of the 20th International Machine Tool Design and Research Conference, pp 525–531Google Scholar
  46. 46.
    Waghodekar PH, Sahu S (1984) Machine-component cell formation in-group technology: MACE. Int J Prod Res 22:937–948CrossRefGoogle Scholar
  47. 47.
    Wemmerlov U (1984) Comments on direct clustering algorithm for group formation in cellular manufacture. J Manuf Syst 3Google Scholar
  48. 48.
    Wemmerlov U, Hyer NL (1986) Procedures for the part family/machine group identification problem in cellular manufacturing. J Oper Manag 6:125–147CrossRefGoogle Scholar
  49. 49.
    Wemmerlov U, Hyer NL (1987) Research issues in cellular manufacturing. Int J Prod Res 25:413–431CrossRefGoogle Scholar
  50. 50.
    Yasuda K, Yin Y (2001) A dissimilarity measure for solving the cell formation problem in cellular manufacturing. Int J Comput Ind Eng 39(1):1–17CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Limited 2006

Authors and Affiliations

  1. 1.Department of Mechanical EngineeringSri Ramakrishna Engineering CollegeCoimbatoreIndia
  2. 2.Department of Mechanical EngineeringCoimbatore Institute of TechnologyCoimbatoreIndia

Personalised recommendations