Skip to main content
SpringerLink
Log in
Book cover

International Conference on Computational Collective Intelligence

ICCCI 2009: Computational Collective Intelligence. Semantic Web, Social Networks and Multiagent Systems pp 152–162Cite as

A New Ant Colony Optimization Algorithm with an Escape Mechanism for Scheduling Problems

  • Conference paper

  • 2585 Accesses

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5796))

Abstract

Ant colony optimization (ACO) algorithm is an evolutionary technologyoften used to resolve difficult combinatorial optimization problems, such as single machine scheduling problems, flow shop or job shop scheduling problems, etc. In this study, we propose a new ACO algorithm with an escape mechanism modifying the pheromone updating rules to escape local optimal solutions. The proposed method is used to resolve a single machine total weighted tardiness problem, a flow shop scheduling problem for makespan minimization, and a job shop scheduling problem for makespan minimization. Compared with existing algorithms, the proposed algorithm will resolve the scheduling problems with less artificial ants and obtain better or at least the same, solution quality.

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   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. Pinedo, M.: Scheduling: theory, algorithm, and systems, 2nd edn. Prentice Hall, Englewood Cliffs (2002)

    MATH  Google Scholar 

  2. Belarmino, A.-D.: An SA/TS mixture algorithm for the scheduling tardiness problem. European Journal of Operation Research 88, 516–524 (1996)

    Article  MATH  Google Scholar 

  3. Ben-Daya, M., Al Fawzan, M.: A tabu search approach for the flow shop scheduling problem. European Journal of Operation Research 109, 88–95 (1998)

    Article  MATH  Google Scholar 

  4. James, R.J.W.: Using tabu search to solve the common due date early/tardy machine scheduling problem. Computers and Operations Research 24, 199–208 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  5. Liaw, C.-F.: A tabu search algorithm for the open shop scheduling problem. Computer and Operations Research 26, 109–126 (1999)

    Article  MathSciNet  MATH  Google Scholar 

  6. Liaw, C.-F.: A hybrid genetic algorithm for the open shop scheduling problem. European Journal of Operation Research 124, 28–42 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  7. Park, B.J., Choi, H.R., Kim, H.S.: A hybrid genetic algorithm for the job shop scheduling problems. Computer and Industrial Engineering 45, 597–613 (2003)

    Article  Google Scholar 

  8. Tadahiko, M., Hisao, I., Hideo, T.: Genetic algorithms for flowshop scheduling problems. Computer and Industrial Engineering 30, 1061–1071 (1996)

    Article  Google Scholar 

  9. Bauer, A., Bullnheimer, B., Hartl, R.F., Strauss, C.: Minimizing total tardiness on a single machine using ant colony optimization. In: Proceedings of the 1999 Congress on Evolutionary Computation, pp. 1445–1450. IEEE Press, Los Alamitos (1999)

    Google Scholar 

  10. Bullnheimer, B., Hartl, R.F.: A New Rank Based Version of the Ant colony system – A Computational Study. Central European Journal of Operations Research 7, 25–38 (1999)

    MathSciNet  MATH  Google Scholar 

  11. den Bensten, M., Stützle, T., Dorigo, M.: Ant Colony Optimization for the Total Weighted Tardiness Problem. In: Deb, K., Rudolph, G., Lutton, E., Merelo, J.J., Schoenauer, M., Schwefel, H.-P., Yao, X. (eds.) PPSN 2000. LNCS, vol. 1917, pp. 611–620. Springer, Heidelberg (2000)

    Chapter  Google Scholar 

  12. Bullnheimer, B., Hartl, R.F., Strauss, C.: An improved Ant colony system algorithm for the Vehicle Routing Problem. Annals of Operations Research 89, 319–328 (1999)

    Article  MathSciNet  MATH  Google Scholar 

  13. Colorni, A., Dorigo, M., Maniezzo, V., Trubian, M.: Ant colony system for job shop scheduling. Belgian Journal of Operations Research 34, 39–53 (1994)

    MATH  Google Scholar 

  14. Costa, D., Hertz, A.: Ants can color graphs. Journal of the Operational Research Society 48, 295–305 (1997)

    Article  MATH  Google Scholar 

  15. Gambardella, L.M., Taillard, E., Dorigo, M.: Ant colonies for the quadratic assignment problem. Journal of Operational Research Society 50, 167–176 (1999)

    Article  MATH  Google Scholar 

  16. Li, Y.-M., Xul: An ant colony optimization heuristic for solving maximum independent set problems. In: Fifth International Conference on Computational Intelligence and Multimedia Applications ICCIMA 2003. Proceedings, September 27-30, pp. 206–211 (2003)

    Google Scholar 

  17. Maniezzo, V., Colorni, A.: The ant colony system applied to quadratic assignment problem. IEEE Transactions on Knowledge and Data Engineering 11, 769–784 (1999)

    Article  Google Scholar 

  18. Taillard, E., Gambardella, L.M.: Adaptive Memories for the Quadratic Assignment Problem. Technical Report, IDSIA-87-97, IDSIA, Lugano, Switzerland (1997)

    Google Scholar 

  19. Vincent, T., Nicolas, M., Fabrice, T., Daniel, L.: An Ant Colony Optimization algorithm to solve a 2-machine bicriteria flowshop scheduling problem. European Journal of Operation Research 142, 250–257 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  20. Ying, K.-C., Liao, C.-J.: An ant colony system for permutation flow-shop sequencing. Computers and Operations Research 31, 791–801 (2004)

    Article  MATH  Google Scholar 

  21. Jiang, M.-P.: Application of Ant Colony System in Job-shop Scheduling Problem. Master Thesis, Graduate Institute of Industrial Engineering, National Taiwan University of Science and Technology (2003)

    Google Scholar 

  22. Hsu, C.-Y.: A Study on Particle Swarm Optimization for Discrete Optimization Problems, Master Thesis, Graduate Institute of Industrial Engineering, National Tsing Hua University (2003)

    Google Scholar 

  23. Chen, L.-S.: Ant Colony System for the Single Machine Total Weighted Tardiness Problem with Sequence-dependent Setups. Master Thesis, Graduate Institute of Industrial Engineering, National Taiwan University of Science and Technology (2002)

    Google Scholar 

  24. Palmer, D.S.: Sequencing jobs through a multi-stage process in the minimum total time – a quick method of obtaining a near optimum. Operation Research Quarterly 16, 101–107 (1965)

    Article  Google Scholar 

  25. Giffler, B., Thompson, G.L.: Algorithm for solving production scheduling problems. Operations Research 8, 487–503 (1960)

    Article  MathSciNet  MATH  Google Scholar 

  26. Taillard, E.: Benchmarks for basic scheduling problems. European Journal of Operation Research 64, 278–285 (1993)

    Article  MATH  Google Scholar 

  27. Beasley, J.E.: OR-Library, http://people.brunel.ac.uk/~mastjjb/jeb/info.html

Download references

Author information

Authors and Affiliations

  1. Department of Information Management, Hwa Hsia Institute of Technology, 23554, Taipei County, Taiwan

    Tsai-Duan Lin & Da-Ren Chen

  2. Department of Information Management, National Taiwan University of Science and Technology, Email:M9209004@mail.ntust.edu.tw, 69042, Taipei, Taiwan

    Chuin-Chieh Hsu & Sheng-Yung Chiu

Authors
  1. Tsai-Duan Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chuin-Chieh Hsu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Da-Ren Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sheng-Yung Chiu
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Institute of Informatics, Wroclaw University of Technology, Str. Janiszweskiego 11/17, 50-370, Wroclaw, Poland

    Ngoc Thanh Nguyen

  2. Centre for Complex Software Systems and Services, Swinburne University of Technology, Hawthorn, P.O. Box 218, 3122, Victoria, Australia

    Ryszard Kowalczyk

  3. Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, G#43, Sec. 4, Keelung Rd, 106, Taipei, Taiwan, R.O.C.

    Shyi-Ming Chen

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Lin, TD., Hsu, CC., Chen, DR., Chiu, SY. (2009). A New Ant Colony Optimization Algorithm with an Escape Mechanism for Scheduling Problems. In: Nguyen, N.T., Kowalczyk, R., Chen, SM. (eds) Computational Collective Intelligence. Semantic Web, Social Networks and Multiagent Systems. ICCCI 2009. Lecture Notes in Computer Science(), vol 5796. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04441-0_13

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-04441-0_13

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-04440-3

  • Online ISBN: 978-3-642-04441-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation