Skip to main content
SpringerLink
Log in

An Iterative Clustering Approach Based on Material Flow Requirements for Cellular Designs

  • Published:
Journal of Intelligent & Robotic Systems Aims and scope Submit manuscript

Abstract

This paper presents an iterative clustering process involving the addition of cells to a proposed cellular design to determine the best assignment of machines to cells. The assignment of machines to each cell is based on material flow clustering techniques with the objective of minimizing inter-cell flows. To determine the performance of the proposed clustering approach, comparisons are made with an Exhaustive Search (ES) approach to show the relative optimality.

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. King, J.R.: Machine-component grouping in production flow analysis: an approach using a rank order clustering algorithm. Int. J. Prod. Res. 18(2), 213–232 (1980)

    Article  Google Scholar 

  2. Kumar Suresh, C., Chandrasekharan, M.P.: 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 (1990)

    Article  Google Scholar 

  3. Hyer, N.L., Wemmerlov, U.: Cellular manufacturing in the U.S. industry: a survey of users. Int. J. Prod. Res. 27(9), 1511–1530 (1989)

    Article  Google Scholar 

  4. Wang, J., Roze, C.: Formation of machine cells and part families: a modified p-median model and a comparative study. Int. J. Prod. Res. 35(5), 1259–1286 (1997)

    Article  MATH  Google Scholar 

  5. Onwubolu, G.C., Songore, V.: A tabu search approach to cellular manufacturing system. Prod. Plan. Control 11, 153–164 (2000)

    Article  Google Scholar 

  6. Baykasoglu, A.: A meta-heuristic algorithm to solve quadratic assignment formulations of cell formation problems without presetting number of cells. J. Intell. Manuf. 15, 753–759 (2004)

    Article  Google Scholar 

  7. McAuley, J.: Machine grouping for efficiency production. Prod. Eng. 52, 53–57 (1972)

    Article  Google Scholar 

  8. Wei, J.C., Kern, G.M.: Commonality analysis: a liner cell clustering algorithm for group technology. Int. J. Prod. Res. 27(12), 2053–2062 (1989)

    Article  Google Scholar 

  9. Gupta, T., Seifoddini, H.: Production data based similarity coefficient for machines-component grouping decisions in the design of cellular manufacturing systems. Int. J. Prod. Res. 28, 1248–1269 (1990)

    Article  Google Scholar 

  10. Luong, L.H.S.: A cellular similarity coefficient algorithm for the design manufacturing cells. Int. J. Prod. Res. 31(8), 1757–1766 (1993)

    Article  Google Scholar 

  11. Harhalakis, G.K., Ioannou, G., Minis, I., Nagi, R.: Manufacturing cell formation under random product demand. Int. J. Prod. Res. 32, 4764 (1994)

    Article  Google Scholar 

  12. Nair, G.J., Narendran, T.T.: CASE: a clustering algorithm for cell formation with sequence data. Int. J. Prod. Res. 36, 157–179 (1998)

    Article  MATH  Google Scholar 

  13. McCormick, W.T., Schweitzer, P.J., White, T.W.: Problem decomposition and data reorganization by a clustering technique. Oper. Res. 20, 454–464 (1972)

    Article  Google Scholar 

  14. Chandrasekharan, M.P., Rajagopalan, R.: An ideal see non-hierarchical clustering algorithm for cellular manufacturing. Int. J. Prod. Res. 24(2), 451–464 (1986)

    Article  MATH  Google Scholar 

  15. Chan, H.M., Milner, D.A.: Direct clustering algorithm for group formation in cellular manufacturing. J. Manuf. Syst. 1, 65–74 (1982)

    Article  Google Scholar 

  16. Kusiak, A., Chow, W.S.: Efficient solving of the group technology problem. J. Manuf. Syst. 6, 117–124 (1987)

    Article  Google Scholar 

  17. Seifoddini, H., Wolfe, P.M.: Application of the similarity coefficient method in group technology. IIE Trans. 18, 271–277 (1986)

    Article  Google Scholar 

  18. Choobineh, F.: A framework for the design of cellular manufacturing systems. Int. J. Prod. Res. 26(7), 1161–1172 (1988)

    Article  Google Scholar 

  19. Kusiak, A.: The generalized group technology concept. Int. J. Prod. Res. 25(4), 561–569 (1987)

    Article  Google Scholar 

  20. Rajamani, D., Singh, N., Aneja, Y.P.: Integrated design of cellular manufacturing systems in the presence of alternative process plans. Int. J. Prod. Res. 28(8), 1541–1554 (1990)

    Article  MATH  Google Scholar 

  21. Shafer, S.M., Rogers, D.F.: A goal programming approach to the cell formation problem. J. Oper. Manag. 10(1), 28–43 (1991)

    Article  Google Scholar 

  22. Viswanathan, S.: Configuration cellular manufacturing: a quadratic integer programming formulation and a simple interchange heuristic. Int. J. Prod. Res. 33, 361–376 (1995)

    Article  MATH  Google Scholar 

  23. Chiang, C.-P., Lee, S.-D.: A genetic-based algorithm with the optimal partition approach for the cell formation in bi-directional linear flow layout. Int. J. Comput. Integr. Manuf. 17(4), 367–375 (2004)

    Article  Google Scholar 

  24. Metropolis, N., Rosenbluth, A.W., Rosenbluth, M.N., Teller, A.H.: Equation of state calculation by fast computing machines. J. Chem. Phys. 21, 1087–1092 (1953)

    Article  Google Scholar 

  25. Chen, W.H., Srivastava, B.: Simulated annealing procedures for forming machine cells in group technology. Eur. J. Oper. Res. 75(1), 100–111 (1995)

    Article  Google Scholar 

  26. Sofianopoulou, S.: Manufacturing cells design with alternative process plans and/or replicate machines. Int. J. Prod. Res. 37, 707–720 (1999)

    Article  MATH  Google Scholar 

  27. Gupta, Y., Gupta, M., Kumar, A., Sundaram, C.: A genetic algorithm-based approach to cell composition and layout design problems. Int. J. Prod. Res. 34(2), 447–482 (1996)

    Article  MATH  Google Scholar 

  28. Lee, M.K., Luong, H.S., Abhary, K.: A genetic algorithm based cell design considering alternative routing. Comput. Integr. Manuf. Syst. 10(2), 93–107 (1997)

    Article  Google Scholar 

  29. Cheng, R., Gen, M.: Loop layout design problems in flexible manufacturing systems using genetic algorithms. Comput. Ind. Eng. 34(1), 53–61 (1998)

    Article  Google Scholar 

  30. Mansouri, S.A., Moattar-Husseini, S.M., Zeqordi, S.H.: A genetic algorithm for multiple objective dealing with exceptional elements in cellular manufacturing. Prod. Plan. Control 14, 437–446 (2003)

    Article  Google Scholar 

  31. Khoo, L.P., Lee, S.G., Yin, X.F.: Multiple-objective optimization of machine cell layout using genetic algorithm. Int. J. Comput. Int. Manuf. 16, 140–155 (2003)

    Article  Google Scholar 

  32. Mak, K.L., Wong, Y.S., Wang, X.X.: An adaptive genetic algorithm for Manufacturing Cell Formation. Int. J. Adv. Manuf. Technol. 16, 491–497 (2000)

    Article  Google Scholar 

  33. Onwubolu, G.C., Mutingi, M.: A genetic algorithm approach to cellular manufacturing systems. Comput. Ind. Eng. 39, 125–144 (2001)

    Article  Google Scholar 

  34. Reikek, B., Chambre, A.D.: A multiple objective grouping genetic algorithm for assembly line design. J. Intell. Manuf. 12, 467–485 (2001)

    Article  Google Scholar 

  35. De Lit, P., Delchambre, A., Henrioud, J.M.: An integrated approach for product family and assembly system design. IEEE J. Robot. Autom. 19(2), 324 (2003)

    Article  Google Scholar 

  36. Glover, F.: Future paths for integer programming and links with artificial intelligence. Comput. Oper. Res. 13, 533–549 (1986)

    Article  MATH  MathSciNet  Google Scholar 

  37. Sun, D., Lin, L., Batta, R.: Cell formation using Tabu Search. Comput. Ind. Eng. 28(3), 485–494 (1995)

    Article  Google Scholar 

  38. Lei, D., Wu, Z.: Tabu search approach based on a similarity coefficient for cell formation in generalized group technology. Int. J. Prod. Res. 19(1), 4035–4047 (2005)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Industrial Engineering and Management, I-Shou University, Kaohsiung, 84001, Taiwan, Republic of China

    Nai-Chieh Wei

  2. Department of Industrial and Manufacturing Engineering, Wayne State University, Detroit, MI, 48202, USA

    Olugbenga O. Mejabi

  3. Department of Business and Management, National University of Tainan, Tainan, 700, Taiwan, Republic of China

    Hong Long Chen

Authors
  1. Nai-Chieh Wei
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Olugbenga O. Mejabi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hong Long Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nai-Chieh Wei.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Wei, NC., Mejabi, O.O. & Chen, H.L. An Iterative Clustering Approach Based on Material Flow Requirements for Cellular Designs. J Intell Robot Syst 60, 493–511 (2010). https://doi.org/10.1007/s10846-010-9428-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10846-010-9428-5

Keywords

Search

Navigation