Skip to main content
SpringerLink
Log in
Book cover

Workshops on Applications of Evolutionary Computation

EvoWorkshops 2001: Applications of Evolutionary Computing pp 484–494Cite as

A New Approach to Solve Permutation Scheduling Problems with Ant Colony Optimization

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2037))

Abstract

A new approach for solving permutation scheduling problems with Ant Colony Optimization is proposed in this paper. The approach assumes that no precedence constraints between the jobs have to be fulfilled. It is tested with an ant algorithm for the Single Machine Total Weighted Deviation Problem. The new approach uses ants that allocate the places in the schedule not sequentially, as the standard approach, but in random order. This leads to a better utilization of the pheromone information. It is shown that adequate combinations between the standard approach which can profit from list scheduling heuristics and the new approach perform particularly well.

This is a preview of subscription content, 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 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

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. A. Colorni, M. Dorigo, V. Maniezzo, and M. Trubian. Ant system for job-shop scheduling. JORBEL-Belgian Journal of Operations Research, Statistics and Computer Science, 34:39–53, 1994.

    MATH  Google Scholar 

  2. M. Dorigo, V. Maniezzo, and A. Colorni. The ant system: Optimization by a colony of cooperating agents. IEEE Trans. Systems, Man, and Cybernetics-Part B, 26:29–41, 1996.

    Article  Google Scholar 

  3. S. van der Zwaan and C. Marques. Ant colony optimisation for job shop scheduling. In Proceedings of the Third Workshop on Genetic Algorithms and Artificial Life (GAAL 99), 1999.

    Google Scholar 

  4. T. Stützle. An ant approach for the flow shop problem. In Proceedings of the 6th European Congress on Intelligent Techniques & Soft Computing (EUFIT’ 98), volume 3, pages 1560–1564. Verlag Mainz, Aachen, 1998.

    Google Scholar 

  5. A. Bauer, B. Bullnheimer, R.F. Hartl, and C. Strauss. An ant colony optimization approach for the single machine total tardiness problem. In Proceedings of the 1999 Congress on Evolutionary Computation (CEC99), 6–9 July Washington D.C., USA, pages 1445–1450, 1999.

    Google Scholar 

  6. M.L den Besten, T. Stützle, and M. Dorigo. Ant colony optimization for the total weighted tardiness problem. In M. Schoenauer et al., editor, Parallel Problem Solving from Nature: 6th international conference, volume 1917 of Lecture Notes on Computer Science, pages 611–620, Berlin, September 2000. Springer Verlag.

    Google Scholar 

  7. D. Merkle and M. Middendorf. An ant algorithm with a new pheromone evaluation rule for total tardiness problems. In Proceeding of the EvoWorkshops 2000, number 1803 in Lecture Notes in Computer Science, pages 287–296. Springer Verlag, 2000.

    Google Scholar 

  8. D. Merkle, M. Middendorf, and H. Schmeck. Ant colony optimization for resource-constrained project scheduling. In D. Whitley et al., editor, Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2000), pages 893–900, Las Vegas, Nevada, USA, 10-12 July 2000. Morgan Kaufmann.

    Google Scholar 

  9. M. Dorigo and G. Di Caro. The ant colony optimization meta-heuristic. In D. Corne, M. Dorigo, and F. Glover, editors, New Ideas in Optimization, pages 11–32. McGraw-Hill, 1999.

    Google Scholar 

  10. http://www.mscmga.ms.ic.ac.uk/jeb/orlib/wtinfo.html.

  11. M.R. Garey and D.S. Johnson. Computers and Intractibility: A Guide to the Theory of NP-Completeness. W.H. Freeman and Company, New York, 1979.

    MATH  Google Scholar 

  12. P.S. Ow and T.E. Morton. The single machine early/tardy problem. Management Science, 35:177–191, 1998.

    Article  MathSciNet  MATH  Google Scholar 

  13. R. Michels and M. Middendorf. An ant system for the shortest common super-sequence problem. In D. Corne, M. Dorigo, and F. Glover, editors, New Ideas in Optimization, pages 51–61. McGraw-Hill, 1999.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute for Applied Computer Science and Formal Description Methods, University of Karlsruhe, Germany

    Martin Middendorf

Authors
  1. Daniel Merkle
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Martin Middendorf
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Leiden University, Niels Bohrweg1, 2333, CA, Leiden, The Netherlands

    Egbert J. W. Boers

Rights and permissions

Reprints and permissions

Copyright information

© 2001 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Merkle, D., Middendorf, M. (2001). A New Approach to Solve Permutation Scheduling Problems with Ant Colony Optimization. In: Boers, E.J.W. (eds) Applications of Evolutionary Computing. EvoWorkshops 2001. Lecture Notes in Computer Science, vol 2037. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45365-2_50

Download citation

  • DOI: https://doi.org/10.1007/3-540-45365-2_50

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-41920-4

  • Online ISBN: 978-3-540-45365-9

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation