A Genetic Algorithm for the Group-Technology Problem

  • Ingo Meents
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2037)


The design and production planning of cellular manufacturing systems requires the decomposition of a company’s manufacturing assets into cells. The set of machines has to be partitioned into machine-groups and the products have to be partitioned into part-families. Finding the machine-groups and their corresponding part-families leads to the combinatorial problem of simultaneously partitioning those two sets with respect to technological requirements represented by the part-machine incidence matrix. This article presents a new solution approach based on a grouping genetic algorithm enhanced by a heuristic motivated by cluster analysis methods.


Genetic Algorithm Genetic Operator Incidence Matrix Group Technology Goal Function 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    S.S. Heragu. Group technology and cellular manufacturing. IEEE Transactions on Systems, Man, and Cybernetics, 24(2):203–215, February 1994.CrossRefGoogle Scholar
  2. 2.
    W.H. Chen and B. Srivastava. Simulated annealing procedures for forming machine cells in group technology. European Journal of Operational Reasearch, 74:100–111, 1994.CrossRefzbMATHGoogle Scholar
  3. 3.
    Y. Gupta, M. Gupta, A. Kumar, and C. Sundaram. A genetic algorithm-based approach to cell composition and layout design problems. International Journal of Production Research, 34(2):447–482, 1996.CrossRefzbMATHGoogle Scholar
  4. 4.
    J.A. Joines, C.T. Culbreth, and R.E. King. Manufacturing cell design: an integer programming model employing genetic algorithms. IIE Transactions, 28(1), 1996.Google Scholar
  5. 5.
    V. Venugopal and T.T. Narendran. A genetic algorithm approach to the machine-component grouping problem with multiple objectives. Computers and Industrial Engineering, 22(4):469–480, 1992.CrossRefGoogle Scholar
  6. 6.
    C. Zaho and Z. Wu. A genetic algorithm for manufacturing cell formation with multiple routes and multiple objectives. International Journal of Production Research, 38(2):385–395, 2000.CrossRefzbMATHGoogle Scholar
  7. 7.
    S.A. Mansouri, S.M. Moattar Husseini, and S.T. Newman. A review of modern approaches to multi-criteria cell design. International Journal of Production Research, 38(5):1201–1218, 2000.CrossRefzbMATHGoogle Scholar
  8. 8.
    I. Meents. Genetic algorithms for the group technology problem. Master’s thesis, Technical University of Clausthal, 1997. (in German).Google Scholar
  9. 9.
    E. Falkenauer. Genetic Algorithms and Grouping Problems. John Wiley & Sons Ltd., Baffins Lane, Chichester, West Sussex PO19 1UD, England, 1998.zbMATHGoogle Scholar
  10. 10.
    Z. Michalewicz. Genetic Algorithms + Data Structures = evolution programs. Springer, Berlin, Heidelberg, New York, 3.edition, 1996.CrossRefzbMATHGoogle Scholar
  11. 11.
    M.P. Chandrasekharan and R. Rajagopalan. An ideal seed non-hierarchical clustering algorithm for cellular manufacturing. International Journal of Production Research, 24(2):451–464, 1986.CrossRefzbMATHGoogle Scholar
  12. 12.
    E. Falkenauer. A new representation and operator for genetic algorithms applied to grouping problems. Evolutionary Computation, 2(2):123–144, 1994.CrossRefGoogle Scholar
  13. 13.
    S.M. Taboun, S. Sankaran, and S. Bhole. Comparison and evaluation of similarity measures in group technology. Computers and Ind. Eng., 20(3):343–353, 1991.CrossRefGoogle Scholar
  14. 14.
    M.P. Chandrasekharan and R. Rajagopalan. ZODIAC-An algorithm for concurrent formation of part-families and machine-cells. International Journal of Production Research, 25(6):835–85, 1987.CrossRefzbMATHGoogle Scholar
  15. 15.
    A. Ballakur and H.J. Steudel. A within-cell utilization based heuristic for designing cellular manufacturing systems. International Journal of Production Research, 25(5):639–665, 1987.CrossRefGoogle Scholar
  16. 16.
    M.P. Chandrasekharan and R. Rajagopalan. MODROC: An extension of rank order clustering for group technology. International Journal of Production Research, 24(5):1221–1233, 1986.CrossRefGoogle Scholar
  17. 17.
    W.S. Chow and O. Hawaleshka. An efficient algorithm for solving the machine chaining problem in cellular manufacturing. Computers and Industrial Engineering, 22(1):95–100, 1992.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Ingo Meents
    • 1
  1. 1.IBM Deutschland Speichersysteme GmbHMainzGermany

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