Advertisement

A Genetic Algorithm for Solving a Dynamic Cellular Manufacturing System

  • Esmaeil MehdizadehEmail author
  • Mansour Shamoradifar
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 803)

Abstract

This paper proposes a genetic algorithm (GA) to solve an integrated mathematical model for dynamic cellular manufacturing system (DCMS) and production planning (PP) concurrently. The model simultaneously seeks to determine the variables associated with the production planning and the cell construction and formation. The total costs include the cost of machine procurement, the cell reconfiguration cost, the cell setup cost, the unexpected variable costs of cells alongside the production planning costs. At first the mathematical model, which is an integer nonlinear programming (INLP), is converted to a linear programming (LP) model. Then, the branch and bound (B&B) method is used for solving small size problems employing the Lingo 8 software. Finally because the problem is NP- hard, a GA is used to solve the large-scale problems as a meta-heuristic algorithm. To evaluate the results obtained by the genetic algorithm, they are compared with those obtained with the Lingo 8 software. Computational results confirm that the genetic algorithm is able to produce good solutions.

References

  1. 1.
    Selim, H.M., Askin, R.G., Vakharia, A.J.: Cell formation in group technology: review, evaluation and directions for future research. Comput. Ind. Eng. 34(1), 3–20 (1998)CrossRefGoogle Scholar
  2. 2.
    Askin, R.G., Estrada, S.: Investigation of Cellular Manufacturing Practices. Wiley, New York (1999)CrossRefGoogle Scholar
  3. 3.
    Wemmerlöv, U., Hyer, N.L.: Cellular manufacturing in the US industry: a survey of users. Int. J. Prod. Res. 27(9), 1511–1530 (1989)CrossRefGoogle Scholar
  4. 4.
    Reisman, A., Kumar, A., Motwani, J., Cheng, C.H.: Cellular manufacturing: a statistical review of the literature (1965–1995). Oper. Res. 45(4), 508–520 (1997)CrossRefGoogle Scholar
  5. 5.
    Rheault, M., Drolet, J.R., Abdulnour, G.: Physically reconfigurable virtual cells: a dynamic model for a highly dynamic environment. Comput. Ind. Eng. 29(1–4), 221–225 (1995)CrossRefGoogle Scholar
  6. 6.
    Balakrishnan, J., Cheng, C.H.: Multi-period planning and uncertainty issues in cellular manufacturing: a review and future directions. Eur. J. Oper. Res. 177(1), 281–309 (2007)CrossRefGoogle Scholar
  7. 7.
    Defersha, F., Chen, M.: A parallel genetic algorithm for dynamic cell formation in cellular manufacturing systems. Int. J. Prod. Res. 46(22), 6389–6413 (2008)CrossRefGoogle Scholar
  8. 8.
    Rezaeian, J., Javadian, N., Tavakkoli-Moghaddam, R., Jolai, F.: A hybrid approach based on the genetic algorithm and neural network to design an incremental cellular manufacturing system. Appl. Soft Comput. 11(6), 4195–4202 (2011)CrossRefGoogle Scholar
  9. 9.
    Mehdizadeh, E., Rahimi, V.: An integrated mathematical model for solving dynamic cell formation problem considering operator assignment and inter/intra cell layouts. Appl. Soft Comput. 42, 325–341 (2016)CrossRefGoogle Scholar
  10. 10.
    Chen, M., Cao, D.: Coordinating production planning in cellular manufacturing environment using Tabu search. Comput. Ind. Eng. 46(3), 571–588 (2004)CrossRefGoogle Scholar
  11. 11.
    Bulgak, A.A., Bektas, T.: Integrated cellular manufacturing systems design with production planning and dynamic system reconfiguration. Eur. J. Oper. Res. 192(2), 414–428 (2009)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Aghajani-Delavar, N., Mehdizadeh, E., Torabi, S., Tavakkoli-Moghaddam, R.: Design of a new mathematical model for integrated dynamic cellular manufacturing systems and production planning. Int. J. Eng.-Trans. B Appl. 28(5), 746 (2014)Google Scholar
  13. 13.
    Mehdizadeh, E., Niaki, S.V.D., Rahimi, V.: A vibration damping optimization algorithm for solving a new multi-objective dynamic cell formation problem with workers training. Comput. Ind. Eng. 101, 35–52 (2016)CrossRefGoogle Scholar
  14. 14.
    Sakhaii, M., Tavakkoli-Moghaddam, R., Bagheri, M., Vatani, B.: A robust optimization approach for an integrated dynamic cellular manufacturing system and production planning with unreliable machines. Appl. Mathematical Modelling 40(1), 169–191 (2016)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Rafiei, H., Ghodsi, R.: A bi-objective mathematical model toward dynamic cell formation considering labor utilization. Appl. Math. Model. 37(4), 2308–2316 (2013)CrossRefGoogle Scholar
  16. 16.
    Kia, R., Khaksar-Haghani, F., Javadian, N., Tavakkoli-Moghaddam, R.: Solving a multi-floor layout design model of a dynamic cellular manufacturing system by an efficient genetic algorithm. J. Manuf. Syst. 33(1), 218–232 (2014)CrossRefGoogle Scholar
  17. 17.
    Deep, K., Singh, P.K.: Design of robust cellular manufacturing system for dynamic part population considering multiple processing routes using genetic algorithm. J. Manuf. Syst. 35, 155–163 (2015)CrossRefGoogle Scholar
  18. 18.
    Holland, J.H.: Adaptation in Natural and Artificial Systems: An Introductory Analysis with Application to Biology, Control, and Artificial Intelligence, pp. 439–444. University of Michigan Press, Ann Arbor (1975)Google Scholar
  19. 19.
    Goldberg, D.E.: Genetic Alogorithms in Search (Optimization and Machine Learning). Addison-Wesley, Massachusetts (1989)Google Scholar
  20. 20.
    Gupta, Y.P., Gupta, M.C., Kumar, A., Sundram, C.: Minimizing total intercell and intracell moves in cellular manufacturing: a genetic algorithm approach. Int. J. Comput. Integr. Manuf. 8(2), 92–101 (1995)CrossRefGoogle Scholar
  21. 21.
    Deljoo, V., Mirzapour Al-e-hashem, S., Deljoo, F., Aryanezhad, M.: Using genetic algorithm to solve dynamic cell formation problem. Appl. Math. Model. 34(4), 1078–1092 (2010)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Faculty of Industrial and Mechanical Engineering, Qazvin BrachIslamic Azad UniversityQazvinIran

Personalised recommendations