Skip to main content

A Hybrid Immune Algorithm for Sequencing the Mixed-Model Assembly Line with Variable Launching Intervals

  • Conference paper

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 105))

Abstract

A challenging multi-objective sequencing problem with variable launching intervals has been studied. A novel hybrid algorithm based on a multi-objective clonal selection algorithm and a co-evolutionary algorithm has been developed for the system control. The clonal selection algorithm for the multi-objective sequencing models is worked as a driving system, while the co-evolutionary immune algorithm for acquiring launching intervals is subordinated and run in parallel on distributed systems in order to guarantee the real-time requirements. The evolution operators such as coding, decoding and collaboration formation mechanism are defined. The scheme has been proven to improve the system optimization and achieve better solution sets as compared with other available algorithms.

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

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
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

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Tavakkoli-Moghaddam, R., Rahimi-Vahed, A.R.: A Memetic Algorithm for Multi-criteria Sequencing Problem for a Mixed-Model Assembly Line in a JIT Production System. In: 2006 IEEE Congress on Evolutionary Computation, pp. 2993–2998. IEEE Press, Vancouver (2006)

    Chapter  Google Scholar 

  2. Rahimi-Vahed, A., Mirzaei, A.H.: A hybrid multi-objective shuffled frog-leaping algorithm for a mixed-model assembly line sequencing problem. J. Computers & Industrial Engineering 53(4), 642–666 (2007)

    Article  Google Scholar 

  3. Boysen, N., Fledner, M., Scholl, A.: Sequencing Mixed-model Assembly Lines: Survey, Classification and Model Critique. J. European Journal of Operational Research 192, 349–373 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  4. Bock, S., Rosenberg, O., Brackel, T.: Controlling Mixed-model Assembly Lines in Real-time by Using Distributed Systems. J. European Journal of Operational Research 168, 880–894 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  5. Fattahi, P., Mohsen, S.: Sequencing the Mixed-model Assembly Line to Minimize the Total Utility and Idle Costs with Variable Launching Interval. J. International Journal of Advanced Manufacturing Technology 45, 987–988 (2009)

    Article  Google Scholar 

  6. Tse, G.T., Hui, K.L., Jason, T.: Cooperative Coevolution for Pareto Multiobjective Optimization: An Empirical Study using SPEA2. In: TENCON 2007 - 2007 IEEE Region 10 Conference, pp. 1–4. IEEE Press, Taipei (2007)

    Google Scholar 

  7. Ramin, H., Saeed, B.S.: Symbiotic artificial immune system. J. Soft Computing 13, 565–575 (2008)

    Google Scholar 

  8. Tan, K.C., Yang, Y.J., Lee, T.H.: A Distributed Cooperative Coevolutionary Algorithm for Multiobjective Optimization. J. IEEE Transaction on Evolutionary Computation 10, 2513–2520 (2006)

    Google Scholar 

  9. Deb, K., Pratap, A., Agarwal, S., et al.: A Fast and Elitist Multi-objective Genetic Algorithm: NSGA-II. J. IEEE Transaction on Evolutionary Computation 6, 182–197 (2002)

    Article  Google Scholar 

  10. Rivera, W.: Scalable Parallel Genetic Algorithms. J. Artificial Intelligence Review 16, 153–168 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  11. Zilzler, E., Deb, K., Thiele, L.: Comparison of Multiobjective Evolutionary Algorithms: Empirical Results. J. Evolutionary Computation 8, 173–195 (2000)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Liu, R., Lou, P., Tang, D., Yang, L. (2010). A Hybrid Immune Algorithm for Sequencing the Mixed-Model Assembly Line with Variable Launching Intervals. In: Zhu, R., Zhang, Y., Liu, B., Liu, C. (eds) Information Computing and Applications. ICICA 2010. Communications in Computer and Information Science, vol 105. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16336-4_53

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-16336-4_53

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-16335-7

  • Online ISBN: 978-3-642-16336-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics