Studs, Seeds and Immigrants in Evolutionary Algorithms for Unrestricted Parallel Machine Scheduling

  • E. Ferretti
  • S. Esquivel
  • R. Gallard
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3315)

Abstract

Parallel machine scheduling, involves the allocation of jobs to the system resources (a bank of machines in parallel). A basic model consisting of m machines and n jobs is the foundation of more complex models. Here, jobs are allocated according to resource availability following some allocation rule. In the specialised literature, minimisation of the makespan has been extensively approached and benchmarks can be easily found. This is not the case for other important objectives such as the due date related objectives. To solve the unrestricted parallel machine scheduling problem, this paper proposes MCMP-SRI and MCMP-SRSI, which are two multirecombination schemes that combine studs, random and seed immigrants. Evidence of the improved behaviour of the EAs when inserting problem-specific knowledge with respect to SCPC (an EA without multirecombination) is provided. Experiments and results are discussed.

Keywords

Schedule Problem Evolutionary Algorithm Short Processing Time Longe Processing Time Random Immigrant 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, log in to check access.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Beasley, J.E.: Weighted Tardiness. OR Library, http://mscmga.ms.ic.ac.uk/
  2. 2.
    Crauwels, H.A.J., Potts, C.N., Van Wassenhove, L.N.: Local Search Heuristics for the Single Machine Total Weighted Tardiness Scheduling Problem. Informs Journal on Computing 10, 341–350 (1998)MATHCrossRefMathSciNetGoogle Scholar
  3. 3.
    Eiben, A.E., Raué, P.E., Ruttkay, Z.: Genetic Algorithms with Multi-parent Recombination. In: Davidor, Y., Männer, R., Schwefel, H.-P. (eds.) PPSN 1994. LNCS, vol. 866, pp. 78–87. Springer, Heidelberg (1994)Google Scholar
  4. 4.
    Eiben, A.E., Van Kemenade, C.H.M., Kok, J.N.: Orgy in the Computer: Multiparent Reproduction in Genetic Algorithms. In: Morán, F., Merelo, J.J., Moreno, A., Chacon, P. (eds.) ECAL 1995. LNCS (LNAI), vol. 929, pp. 934–945. Springer, Heidelberg (1995)Google Scholar
  5. 5.
    Eiben, A.E., Bäck, T.: An Empirical Investigation of Multi-parent Recombination Operators in Evolution Strategies. Evolutionary Computation 5(3), 347–365 (1997)CrossRefGoogle Scholar
  6. 6.
    Esquivel, S., Leiva, A., Gallard, R.: Multiple Crossover per Couple in Genetic Algorithms. In: Evolutionary Computation (ICEC 1997), April 1997, pp. 103–106. IEEE Publishing Co, Indianapolis (1997) ISBN 0-7803-3949-5Google Scholar
  7. 7.
    Esquivel, S., Leiva, A., Gallard, R.: Couple Fitness Based Selection with Multiple Crossover Per Couple in Genetic Algorithms. In: Proceedings del International Symposium on Engineering of Intelligent Systems, February 1998, vol. 1, pp. 235–241. University of La Laguna, Tenerife (1998) ISBN 3-906454-12-6Google Scholar
  8. 8.
    Esquivel, S., Leiva, H., Gallard, R.: Multiple Crossovers between Multiple Parents to Improve Search in Evolutionary Algorithms. In: Evolutionary Computation, pp. 1589–1594. IEEE Publishing Co, Washington DC (1999)Google Scholar
  9. 9.
    Esquivel, S., Ferrero, S., Gallard, R., Salto, C., Alfonso, H., Schütz, M.: Enhanced Evolutionary Algorithms for Single and Multiobjective Optimization in the Job Shop Scheduling Problem. Knowledge-Based Systems 15, 13–25 (2002)CrossRefGoogle Scholar
  10. 10.
    Feltl, H., Raidl, G.: An Improved Hybrid Genetic Algorithm for the Generalized Assignment Problem. In: Proceedings of the 2004 ACM symposium on Applied computing, Nicosia, Cyprus, ECO, pp. 990–995 (2004) ISBN:1-58113-812-1Google Scholar
  11. 11.
    Liu, M., Wu, C.: Scheduling Algorithm based on Evolutionary Computing in Identical Parallel Machine Production Line. Robotics and Computer-Integrated Manufacturing 19, 401–407 (2003)CrossRefGoogle Scholar
  12. 12.
    Morton, T., Pentico, D.: Heuristic Scheduling Systems. Wiley series in Engineering and technology management. John Wiley and Sons, INC (1993)Google Scholar
  13. 13.
    Pandolfi, D., Vilanova, G., De San Pedro, M., Villagra, A., Gallard, R.: Multirecombining Studs and Immigrants in Evolutionary Algorithm to face Earliness-Tardiness Scheduling Problems. In: Proceedings of the International Conference in Soft Computing, June 2001, p. 138. University of Paisley, Scotland (2001)Google Scholar
  14. 14.
    Pandolfi, D., De San Pedro, M., Villagra, A., Vilanova, G., Gallard, R.: Studs Mating Immigrants in Evolutionary Algorithm to Solve the Earliness-Tardiness Scheduling Problem. In Cybernetics and Systems of Taylor and Francis Journal 33(4), 391–400 (2002)MATHCrossRefGoogle Scholar
  15. 15.
    Pandolfi, D., De San Pedro, M., Villagra, A., Vilanova, G., Gallard, R.: Multirecombining Random and Seed Immigrants in Evolutionary Algorithms to Solve W-T Scheduling Problems. In: proceedings of CSITeA02, June 2002, pp. 133–138. Iguazu Falls, Brazil (2002)Google Scholar
  16. 16.
    Pinedo, M.: Scheduling: Theory, Algorithms and System, 1st edn. Prentice Hall, Englewood Cliffs (1995)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • E. Ferretti
    • 1
  • S. Esquivel
    • 1
  • R. Gallard
    • 1
  1. 1.Laboratorio de Investigación y Desarrollo en Inteligencia ComputacionalUniversidad Nacional de San LuisArgentina

Personalised recommendations