Skip to main content
SpringerLink
Log in

Some Aspects Regarding the Application of the Ant Colony Meta-heuristic to Scheduling Problems

  • Conference paper
Large-Scale Scientific Computing (LSSC 2009)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5910))

Included in the following conference series:

  • 2111 Accesses

Abstract

Scheduling is one of the most complex problems that appear in various fields of activity, from industry to scientific research, and have a special place among the optimization problems. In our paper we present the results of our computational study i.e. an Ant Colony Optimization algorithm for the Resource-Constrained Project Scheduling Problem that uses dynamic pheromone evaporation.

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 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

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Abdelaziz, F.B., Krichen, S., Dridi, O.: A Multiobjective Resource-Constrained Project-Scheduling Problem. In: Mellouli, K. (ed.) ECSQARU 2007. LNCS (LNAI), vol. 4724, pp. 719–730. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  2. Angus, D.: Ant Colony Optimization: From Biological Inspiration to an Algorithmic Framework. Technical Report: TR013, Centre for Intelligent Systems & Complex Processes, Faculty of Information & Communication Technologies, Swinburne University of Technology Melbourne, Australia (2006)

    Google Scholar 

  3. Blum, C.: Theoretical and Practical Aspects of Ant Colony Optimization. Dissertations in Artificial Intelligence, vol. 282. Akademische Verlagsgesellschaft Aka GmbH, Berlin (2004)

    Google Scholar 

  4. Brucker, P.: Scheduling Algorithms. Springer, Heidelberg (2001)

    MATH  Google Scholar 

  5. Cook, W.J., Cunningham, W.H., Pulleyblank, W.R., Schrijver, A.: Combinatorial Optimization, 1st edn. John Wiley & Sons, Chichester (1997)

    Google Scholar 

  6. Dorigo, M.: Optimization, learning and natural algorithms, Ph.D. Thesis, Dip Elettronica e Informazione, Politecnico di Milano, Italy (1992)

    Google Scholar 

  7. Dorigo, M., Birattari, M., Stützle, T.: Ant Colony Optimization. Artificial Ants as a Computational Intelligence Technique, IRIDIA — Technical Report Series Technical Report No. TR/IRIDIA/2006-023 (September 2006)

    Google Scholar 

  8. Dorigo, M., Stützle, T.: Ant Colony Optimization. MIT Press, Cambridge (2005)

    Google Scholar 

  9. Hartman, S.: A Self-Adapting Genetic Algorithm for Project Scheduling under Resource Constraints. Naval Research Logistics 49, 433–448 (2002)

    Article  MathSciNet  Google Scholar 

  10. Păun, G.: Membrane computing: some non-standard ideas. In: Jonoska, N., Păun, G., Rozenberg, G. (eds.) Aspects of Molecular Computing. LNCS, vol. 2950, pp. 322–337. Springer, Heidelberg (2003)

    Google Scholar 

  11. Lenstra, J.K., Rinnooy Kan, A.H.G.: Complexity of Scheduling under Precedence Constraints. Oper. Res. 26, 22–35 (1978)

    Article  MATH  MathSciNet  Google Scholar 

  12. Daniel, M., Middendorf, M., Schmeck, H.: Pheromone Evaluation in Ant Colony Optimization. In: 26th Annual Conf. of the IEEE, vol. 4, pp. 2726–2731 (2000)

    Google Scholar 

  13. Merkle, D., Middendorf, M., Schmeck, H.: Ant Colony Optimization for Resource-Constrained Project Scheduling. IEEE Transactions on Evolutionary Computation 6(4), 333–346 (2002)

    Article  Google Scholar 

  14. Olteanu, A.-L.: Ant Colony Optimization Meta-Heuristic in Project Scheduling. In: 8th WSEAS International Conference on Artificial Intelligence, Knowledge Engineering and Data Bases, World Scientific and Engineering Academy and Society (WSEAS), Stevens Point, Wisconsin, USA, pp. 29–34 (2009)

    Google Scholar 

  15. Papadimitriou, C.H., Steiglitz, K.: Combinatorial Optimization: Algorithms and Complexity. Dover Pubns. (1998) ISBN 0-486-40258-4

    Google Scholar 

  16. Paun, G.: Membrane computing — An Introduction. Natural Computing Series. Springer, Heidelberg (2002)

    MATH  Google Scholar 

  17. Stützle, T., Dorigo, M.: ACO algorithms for the Traveling Salesman Problem. Evolutionary Algorithms in Engineering and Computer Science. In: Evolutionary Algorithms in Engineering and Computer Science. Recent Advances in Genetic Algorithms, Evolution Strategies, Evolutionary Programming, Genetic Programming and Industrial Applications, ch. 9, pp. 163–184. Wiley, Chichester (1999)

    Google Scholar 

  18. Tavares, L.V., Weglarz, J.: Project Management and Scheduling: A Permanent Challenge for OR. European Journal of Operational Research 49(1), 1–2 (1990)

    Article  Google Scholar 

  19. Valente, J.M.S., Alves, R.A.F.S.: Beam-search Algorithms for the early/tardy Scheduling Problem with Release Dates Investigação – Trabalhos em curso  143 (2004)

    Google Scholar 

  20. Wall, M.B.: A Genetic Algorithm for Resource-Constrained Scheduling. MIT Press, Cambridge (1996)

    Google Scholar 

  21. Project Scheduling Problem Library, http://129.187.106.231/psplib/main.html

Download references

Author information

Authors and Affiliations

  1. Faculty of Engineering, “Lucian Blaga” University of Sibiu, Str. Emil Cioran, Nr. 4, Sibiu, 550025, Romania

    Ioana Moisil & Alexandru-Liviu Olteanu

Authors
  1. Ioana Moisil
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alexandru-Liviu Olteanu
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Institute for Parallel Processing, Bulgarian Academy of Sciences, Acad. G. Bonchev, Bl. 25A, 1113, Sofia, Bulgaria

    Ivan Lirkov

  2. Institute for Parallel Processing, Bulgarian Academy of Sciences, Acad. G. Bonchev Str. Bl. 25-A, 1113, Sofia, Bulgaria

    Svetozar Margenov

  3. Department of Informatics and Mathematical Modelling, Technical University of Denmark, Richard Petersens Plads - Building 321, 2800, Kongens Lyngby, Denmark

    Jerzy Waśniewski

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Moisil, I., Olteanu, AL. (2010). Some Aspects Regarding the Application of the Ant Colony Meta-heuristic to Scheduling Problems. In: Lirkov, I., Margenov, S., Waśniewski, J. (eds) Large-Scale Scientific Computing. LSSC 2009. Lecture Notes in Computer Science, vol 5910. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12535-5_40

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-12535-5_40

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-12534-8

  • Online ISBN: 978-3-642-12535-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation