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

  • Tsai-Duan Lin
  • Chuin-Chieh Hsu
  • Da-Ren Chen
  • Sheng-Yung Chiu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, 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.

Keywords

Ant colony optimization escape mechanism combinatorial scheduling makespan 

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References

  1. 1.
    Pinedo, M.: Scheduling: theory, algorithm, and systems, 2nd edn. Prentice Hall, Englewood Cliffs (2002)zbMATHGoogle Scholar
  2. 2.
    Belarmino, A.-D.: An SA/TS mixture algorithm for the scheduling tardiness problem. European Journal of Operation Research 88, 516–524 (1996)CrossRefzbMATHGoogle Scholar
  3. 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)CrossRefzbMATHGoogle Scholar
  4. 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)MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Liaw, C.-F.: A tabu search algorithm for the open shop scheduling problem. Computer and Operations Research 26, 109–126 (1999)MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    Liaw, C.-F.: A hybrid genetic algorithm for the open shop scheduling problem. European Journal of Operation Research 124, 28–42 (2000)MathSciNetCrossRefzbMATHGoogle Scholar
  7. 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)CrossRefGoogle Scholar
  8. 8.
    Tadahiko, M., Hisao, I., Hideo, T.: Genetic algorithms for flowshop scheduling problems. Computer and Industrial Engineering 30, 1061–1071 (1996)CrossRefGoogle Scholar
  9. 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. 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)MathSciNetzbMATHGoogle Scholar
  11. 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)CrossRefGoogle Scholar
  12. 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)MathSciNetCrossRefzbMATHGoogle Scholar
  13. 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)zbMATHGoogle Scholar
  14. 14.
    Costa, D., Hertz, A.: Ants can color graphs. Journal of the Operational Research Society 48, 295–305 (1997)CrossRefzbMATHGoogle Scholar
  15. 15.
    Gambardella, L.M., Taillard, E., Dorigo, M.: Ant colonies for the quadratic assignment problem. Journal of Operational Research Society 50, 167–176 (1999)CrossRefzbMATHGoogle Scholar
  16. 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. 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)CrossRefGoogle Scholar
  18. 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. 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)MathSciNetCrossRefzbMATHGoogle Scholar
  20. 20.
    Ying, K.-C., Liao, C.-J.: An ant colony system for permutation flow-shop sequencing. Computers and Operations Research 31, 791–801 (2004)CrossRefzbMATHGoogle Scholar
  21. 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. 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. 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. 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)CrossRefGoogle Scholar
  25. 25.
    Giffler, B., Thompson, G.L.: Algorithm for solving production scheduling problems. Operations Research 8, 487–503 (1960)MathSciNetCrossRefzbMATHGoogle Scholar
  26. 26.
    Taillard, E.: Benchmarks for basic scheduling problems. European Journal of Operation Research 64, 278–285 (1993)CrossRefzbMATHGoogle Scholar
  27. 27.

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Tsai-Duan Lin
    • 1
  • Chuin-Chieh Hsu
    • 2
  • Da-Ren Chen
    • 1
  • Sheng-Yung Chiu
    • 2
  1. 1.Department of Information ManagementHwa Hsia Institute of TechnologyTaipei CountyTaiwan
  2. 2.Department of Information ManagementNational Taiwan University of Science and Technology, Email:M9209004@mail.ntust.edu.twTaipeiTaiwan

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