A Genetic Algorithm with Local Search for Solving Job Problems

  • L W Cai
  • Q H Wu
  • Z Z Yong
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1803)

Abstract

This paper presents a genetic algorithm specially designed for job shop problems. The algorithm has a simple coding scheme and new crossover and mutation operators. A simple local search scheme is incorporated in the algorithm leading to a combined genetic algorithm(CGA). It is evaluated in three famous Muth and Thompson problems (i.e. MT6×6, MT10×10, MT20×5). The simulation study shows that this algorithm possesses high efficiency and is able to find out the optimal solutions for the job shop problems.

Keywords

Combined genetic algorithm local search job shop scheduling 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Coffman, E. G., et al., Computer and Job-Shop Scheduling Theory, U.S.A., John Wiley & Sons, 1976.MATHGoogle Scholar
  2. 2.
    French, S., Sequencing and Scheduling: An Introduction to The Mathematics of The Job-Shop, England, Ellis Horwood Ltd., 1982.MATHGoogle Scholar
  3. 3.
    Barker, J. R. and McMahon, G. B., ‘Scheduling the general job shop’, Management Science, 1985, 31(5), 594–598.MATHCrossRefGoogle Scholar
  4. 4.
    Carlier, J. and Pinson, E., ‘An algorithm for solving the job-shop problem’, Management Science, 1989, 35(2), 164–176.MATHMathSciNetGoogle Scholar
  5. 5.
    Goldberg, D. E., Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley Publishing Company, 1989.Google Scholar
  6. 6.
    Starkweather, T., et al., ‘A comparison of genetic sequencing operators’, Proceedings of The Fourth International Conference on Genetic Algorithms, SAN DIEGO, 1991, pp.69–76 41.Google Scholar
  7. 7.
    Bagchi, S., et al., ‘Exploring problem-specific recombination operators for job shop scheduling’, Proceedings of The Fourth International Conference on Genetic Algorithms, SAN DIEGO, 1991, pp.10–17.Google Scholar
  8. 8.
    Cao, Y.J., Wu, Q.H., ‘Mechanical design optimization by mixed-variable evolutionary programming’, Proc. IEEE International Conference on Evolutionary Computation, 1997, Indianapolis, USA, pp.443–446.Google Scholar
  9. 9.
    Wu, Q.H., Cao, Y.J., ‘Stochastic optimization of control parameters in genetic algorithms’, Proc. IEEE International Conference on Evolutionary Computation, 1997, Indianapolis, USA., pp77–80.Google Scholar
  10. 10.
    Nakano, R. and Yamada, T., ‘Conventional genetic algorithm for job shop problems’, Proceedings of The Fourth International Conference on Genetic Algorithms, SAN DIEGO, 1991, pp.474–479.Google Scholar
  11. 11.
    Federico Delia Croce, et al., ‘A genetic algorithm for the job shop problem’, Computers & Operations Research, 1995, 22(1), 15–24.MATHCrossRefGoogle Scholar
  12. 12.
    Shi, G., ‘A genetic algorithm applied to a classic job-shop scheduling problem’, International Journal of Systems Science, 1997, 28(1), 25–32.MATHCrossRefGoogle Scholar
  13. 13.
    Yamada, T. and Nakano, R., ‘A genetic algorithm with multi-step crossover for job shop scheduling problems’, First International Conference on Genetic Algorithms in Engineering Systems: Innovations and Applications: GALESIA, 1st, Sheffield, 1995, pp.146–151.Google Scholar
  14. 14.
    Muth, J. F. and Thompson, G. L., Industrial scheduling, Prentice-Hall, Englewood Cliffs, New Jersey, 1963.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • L W Cai
    • 1
  • Q H Wu
    • 2
  • Z Z Yong
    • 1
  1. 1.Department of Electronic EngineeringShenzhen UniversityShenzhenP. R. China
  2. 2.Department of Electrical Engineering and ElectronicsThe University of LiverpoolLiverpoolUK

Personalised recommendations