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A Sequence-Based Cellular Manufacturing System Design Using Genetic Algorithm

  • C. R. Shiyas
  • B. Radhika
  • G. R. Vineetha
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 768)

Abstract

This paper is presented with an algorithm for manufacturing cell system design and part family identification. The model is suitable for establishing a good division of machine cells and part families considering operation sequence data. The aim of this model is the maximization of group technology efficiency value which is mostly used for measuring the worth of cellular configurations when route matrix data is considered in design. Allocating machines to different machine cells is carried out using a randomized procedure based on genetic algorithm. Five situations based on four problems were subjected to comparison based on Group Technology Efficiency (GTE) with two other methods from the literature and it is observed that the new algorithm is either outperforming the other methods or giving the best results obtained from them.

Keywords

Group technology efficiency Cellular manufacturing Genetic algorithm Sequence data 

References

  1. 1.
    Wemmerlo, V.U., Johnson, D.J.: Cellular manufacturing at 46 user plants: implementation experiences and performance improvements. Int. J. Prod. Res. 1(35), 29–49 (1997)CrossRefGoogle Scholar
  2. 2.
    Pillai, V.M., Subbarao, K.A.: Robust cellular manufacturing system design for dynamic part population using a genetic algorithm. Int. J. Prod. Res. 46(1), 5191–5210 (2008)CrossRefGoogle Scholar
  3. 3.
    Adenso-Diaz, B., Lozano, S.: A model for the design of dedicated manufacturing cells. Int. J. Prod. Res. 46, 301–319 (2008)CrossRefGoogle Scholar
  4. 4.
    Chen, C.L., Cotruvo, N.A., Baek, W.: A simulated annealing solution to the cell formation problem. Int. J. Prod. Res. 33, 2601–2614 (1995)CrossRefGoogle Scholar
  5. 5.
    Nair, J.G., Narendran, T.T.: CASE: A clustering algorithm for cell formationwith sequence data. Int. J. Prod. Res. 36, 157–179 (1998)CrossRefGoogle Scholar
  6. 6.
    Park, S., Suresh, N.C.: Performance of Fuzzy ART neural network and hierarchical clustering for part machine grouping based on operation sequences. Int. J. Prod. Res. vv. 41(14), 3185–3216 (2003)CrossRefGoogle Scholar
  7. 7.
    Won, Y., Lee, K.C.: Group technology cell formation considering operation sequences and production volumes. Int. J. Prod. Res. 39, 2755–2768 (2001)CrossRefGoogle Scholar
  8. 8.
    Shiyas, C.R., Madhusudanan, Pillai V.: An algorithm for intra-cell machine sequence identification for manufacturing cells. Int. J. Prod. Res. 5, 2427–2433 (2014)Google Scholar
  9. 9.
    Alijuneidi, T., Bulgak, A.: A: designing a cellular manufacturing system featuring remanufacturing, recycling, and disposal options: a mathematical modeling approach. CIRP J. Manufact. Sci. Technol. 19, 25–35 (2017)CrossRefGoogle Scholar
  10. 10.
    Kumar, C.S., 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, 233–243 (1990)CrossRefGoogle Scholar
  11. 11.
    Harhalakis, G., Nagi, R., Proth, J.M.: An efficient heuristic in manufacturingcell formation for group technology applications. Int. J. Prod. Res. 28, 185–198 (1990)CrossRefGoogle Scholar
  12. 12.
    SudhakaraPandian, R., Mahapatra, S.S.: Manufacturing cell formation with production data using neural networks. Comput. Ind. Eng. 56, 1340–1347 (2009)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Cochin University College of Engineering KuttanaduPulincunnoo, AlappuzhaIndia

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