Skip to main content
SpringerLink
Log in

Solving Manufacturing Cell Design Problems Using an Artificial Fish Swarm Algorithm

  • Conference paper
  • First Online:
Advances in Artificial Intelligence and Soft Computing (MICAI 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9413))

Included in the following conference series:

Abstract

The design of manufacturing cells is a manufacturing strategy that involves the creation of an optimal design of production plants, whose main objective is to minimize movements and exchange of material between these cells. Optimal solution of large scale manufacturing cell design problems (MCDPs) are often computationally unfeasible and only heuristic and approximate methods are able to handle such problems. Artificial fish swarm algorithm (AFSA) belongs to the swarm intelligence algorithms, which based on population search, are able to solve complex optimization problems. In this paper we present an AFSA-based approach to solve the MCDP by using the classic Boctor’s mathematical model. The obtained results show that the proposed algorithm produces optimal solutions for all the 50 studied instances.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Li, L.X., Shao, Z.J., Qian, J.X.: An optimizing method based on autonomous animate: fish swarm algorithm. In: Proceeding of System Engineering Theory and Practice, pp. 32–38 (2002)

    Google Scholar 

  2. Hi, S., Belacel, N., Hamam, H., Bouslimani, Y.: Fuzzy clustering with improved artificial fish swarm algorithm. In: International Joint Conference on Computational Sciences and Optimization 2009, Hainan, pp. 317–321 (2009)

    Google Scholar 

  3. Xiao, L.: A clustering algorithm based on artificial fish swarm. In: 2nd International Conference on Computer Engineering and Technology, Chengdu, pp. 766–769 (2010)

    Google Scholar 

  4. Yazdani, D., Golyari, S., Meybodi, M.R.: A new hybrid algorithm for optimization based on artificial fish swarm algorithm and cellular learning automata. In: 5 International Symposium on Telecommunication (IST), Tehran, pp. 932–937 (2010)

    Google Scholar 

  5. Yazdani, D., Nadjaran Toosi, A., Meybodi, M.R.: Fuzzy adaptive artificial fish swarm algorithm. In: 23rd Australian Conference on Artificial Intelligent, Adelaide (2010)

    Google Scholar 

  6. Luo, Y., Zhang, J., Li, X.: The optimization of PID controller parameters based on artificial fish swarm algorithm. In: IEEE International Conference on Automation and Logistics, Jinan, pp. 1058–1062 (2007)

    Google Scholar 

  7. Zhang, M., Shao, C., Li, M., Sun, J.: Mining classification rule with artificial fish swarm. In: 6 World Congress on Intelligent Control and Automation, Dalian, pp. 5877–5881 (2006)

    Google Scholar 

  8. Li, C.X., Ying, Z., JunTao, S., Qing, S.J.: Method of image segmentation based on fuzzy c-means clustering algorithm and artificial fish swarm algorithm. In: International Conference on Intelligent Computing and Integrated Systems (ICISS), Guilin (2010)

    Google Scholar 

  9. Xambre, A.R., Vilarinho, P.M.: A simulated annealing approach for manufacturing cell formation with multiple identical machines. Eur. J. Oper. Res. 151, 434–446 (2003)

    Article  MATH  MathSciNet  Google Scholar 

  10. Kusiak, A.: The part families problem in flexible manufacturing systems. Ann. Oper. Res. 3, 279–300 (1985)

    Article  Google Scholar 

  11. Shargal, M., Shekhar, S., Irani, S.A.: Evaluation of search algorithms and clustering efficiency measures for machine-part matrix clustering. IIE Trans. 27(1), 43–59 (1995)

    Article  Google Scholar 

  12. Seifoddini, H., Hsu, C.-P.: Comparative study of similarity coefficients and clustering algorithms in cellular manufacturing. J. Manuf. Syst. 13(2), 119–127 (1994)

    Article  Google Scholar 

  13. Srinivasan, G.: A clustering algorithm for machine cell formation in group technology using minimum spanning tree. Int. J. Prod. Res. 32(9), 2149–2158 (1994)

    Article  MATH  Google Scholar 

  14. Deutsch, S.J., Freeman, S.F., Helander, M.: Manufacturing cell formation using an improved p-median model. Comput. Ind. Eng. 34(1), 135–146 (1998)

    Article  Google Scholar 

  15. Atmani, A., Lashkari, R.S., Caron, R.J.: A mathematical programming approach to joint cell formation and operation allocation in cellular manufacturing. Int. J. Prod. Res. 33(1), 1–15 (1995)

    Article  MATH  Google Scholar 

  16. Adil, G.K., Rajamani, D., Strong, D.: A mathematical model for cell formation considering investment and operational costs. Eur. J. Oper. Res. 69(3), 330–341 (1993)

    Article  MATH  Google Scholar 

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

    Article  Google Scholar 

  18. Purcheck, G.: A linear-programming method for the combinatorial grouping of an incomplete set. J. Cybern. 5, 51–58 (1975)

    Article  MathSciNet  Google Scholar 

  19. Olivia-Lopez, E., Purcheck, G.: Load balancing for group technology planning and control. Int. J. MTDR 19, 259–268 (1979)

    Google Scholar 

  20. Soto, R., Kjellerstrand, H., Durn, O., Crawford, B., Monfroy, E., Paredes, F.: Cell formation in group technology using constraint programming and Boolean satisfiability. Expert Syst. Appl. 39, 11423–11427 (2012)

    Article  Google Scholar 

  21. Boctor, F.F.: A linear formulation of the machine-part cell formation problem. Int. J. Prod. Res. 29(2), 343–356 (1991)

    Article  Google Scholar 

  22. Durn, O., Rodriguez, N., Consalter, L.: Collaborative particle swarm optimization with a data mining technique for manufacturing cell design. Expert Syst. Appl. 37(2), 1563–1567 (2010)

    Article  Google Scholar 

  23. Wu, T., Chang, C., Chung, S.: A simulated annealing algorithm for manufacturing cell formation problems. Expert Syst. Appl. 34(3), 1609–1617 (2008)

    Article  Google Scholar 

  24. Venugopal, V., Narendran, T.T.: A genetic algorithm approach to the machine-component grouping problem with multiple objectives. Comput. Ind. Eng. 22(4), 469–480 (1992)

    Article  Google Scholar 

  25. 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 

  26. Yazdani, D., Golyari, S., Reza, M.M.: A new hybrid approach for data clustering. In: 5th International Symposium on Telecommunication (IST), Tehran, pp. 932–937 (2010)

    Google Scholar 

  27. Wang, L., Ma, L.: A hybrid artificial fish swarm algorithm for bin-packing problem. In: International Conference on Electronic and Mechanical Engineering and Information Technology, pp. 27–29 (2011)

    Google Scholar 

  28. Zhang, M., et al.: Mining classification rule with artificial fish swarm, pp. 5877–5881 (2006)

    Google Scholar 

Download references

Acknowledgements

Ricardo Soto is supported by Grant CONICYT/FONDECYT/INICIACION/11130459, Broderick Crawford is supported by Grant CONICYT/FONDECYT/1140897, and Fernando Paredes is supported by Grant CONICYT/FONDECYT/1130455.

Author information

Authors and Affiliations

  1. Pontificia Universidad Católica de Valparaíso, Valparaíso, Chile

    Ricardo Soto, Broderick Crawford & Emanuel Vega

  2. Universidad Autónoma de Chile, Temuco, Chile

    Ricardo Soto

  3. Universidad Cientifica del Sur, Lima, Peru

    Ricardo Soto

  4. Universidad Central de Chile, Santiago, Chile

    Broderick Crawford

  5. Universidad San Sebastián, Santiago, Chile

    Broderick Crawford

  6. Escuela de Ingeniería Industrial, Universidad Diego Portales, Santiago, Chile

    Fernando Paredes

Authors
  1. Ricardo Soto
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Broderick Crawford
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Emanuel Vega
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Fernando Paredes
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Emanuel Vega .

Editor information

Editors and Affiliations

  1. Instituto Politécnico Nacional, Centro de Investigación en Computación, Mexico City, Mexico

    Grigori Sidorov

  2. Facultad de ciencias, Universidad Autónoma Nacional, México, Distrito Federal, Mexico

    Sofía N. Galicia-Haro

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Soto, R., Crawford, B., Vega, E., Paredes, F. (2015). Solving Manufacturing Cell Design Problems Using an Artificial Fish Swarm Algorithm. In: Sidorov, G., Galicia-Haro, S. (eds) Advances in Artificial Intelligence and Soft Computing. MICAI 2015. Lecture Notes in Computer Science(), vol 9413. Springer, Cham. https://doi.org/10.1007/978-3-319-27060-9_23

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-27060-9_23

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27059-3

  • Online ISBN: 978-3-319-27060-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation