Advertisement

Solving generic scheduling problems with a distributed genetic algorithm

  • M. McIlhagga
Progress in Evolutionary Scheduling
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1305)

Abstract

This paper describes a Distributed Genetic Algorithm (DGA) which has been used to solve generic scheduling problems. The GA based scheduler allows the user to define and solve any scheduling problem. It does this using a Scheduling Description Language (SDL). The sort of problem that it might tackle are: job-shop scheduling (JJS), time-tabling, resource sequencing etc. We describe a unique chromosome coding scheme that allows simple representation, straightforward chromosome recombination and fast schedule building and therefore evaluation times. A comparative study has been made of the DGA, random search and a heuristic method of scheduling using 100 very large scale problems; problems of the order of 500 tasks. This is the first study of its kind to look at problems of this scale. It was found that, although it is possible to reduce the makespan of a schedule by about \(\tfrac{2}{5}\) of a randomly generated solution using dispatching rules, only the DGA produced solutions that had as high as a \(\tfrac{3}{5}\) reduction.

Keywords

Genetic Algorithm Schedule Problem Random Search Slack Time Schedule Prob 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    C. Bierwirth, D. C. Mattfeld, and H. Kopfer. On permutation representations for scheduling problems. In I. Rechenberg H. Voigt, W. Ebeling and H. Schwefel, editors, Parallel Problem Solving From Nature-PPSN IV, volume 1141 of Lecture Note in Computer Science, pages 960–970. Springer-Verlag, Berlin, 1996.Google Scholar
  2. 2.
    L. Davis. Job-shop scheduling with genetic algorithms. In J. Grefenstette, editor, Proc. Int. Conf. on GAs, pages 136–140. Lawrence Erlbaum, 1985.Google Scholar
  3. 3.
    S. French. Sequencing and scheduling: an introduction to the mathematics of the job-shop. Ellis Horwood, 1982.Google Scholar
  4. 4.
    B. Khoshnevis and Q. Chen. Integration of process planning and scheduling functions. Journal of Intelligent Manufacturing, 1:165–176, 1990.Google Scholar
  5. 5.
    M. McIlhagga. A generic encoding for scheduling problems. Technical report, University of Sussex, 1995.Google Scholar
  6. 6.
    M. McIlhagga. Gpdga user documentation. Technical report, University of Sussex, 1995.Google Scholar
  7. 7.
    M. McIlhagga, P. Husbands, and R. Ives. A comparison of simulated annealing, dispatching rules and a coevolutionary distributed genetic algorithm as optimization techniques for various integrated manufacturing planning problems. problem. In Proceedings of PPSN IV, volume LNCS, 1141, pages 604–613. Springer Verlag, 1996.Google Scholar
  8. 8.
    G. Palmer. An Integrated Approach to Manufacturing Planning. PhD thesis, University of Huddersfield, 1994.Google Scholar
  9. 9.
    K. Sycara, S. Roth, and M. Fox. Resource allocation in disributed factory scheduling. IEEE Expert, pages 29–40, Feb. 1991.Google Scholar
  10. 10.
    H. Tamaki and Y. Nishikawa. Parallelled genetic algorithm based on a neighborhood model and its application to job-shop scheduling. In Proceedings of PPSN II. Springer Verlag, 1992.Google Scholar
  11. 11.
    T. Yamada and R. Nakano. Scheduling by genetic local search with multi-step crossover. In I. Rechenberg H. Voigt, W. Ebeling and H. Schwefel, editors, Parallel Problem Solving From Nature-PPSN IV, volume 1141 of Lecture Note in Computer Science, pages 960–970. Springer-Verlag, Berlin, “1996”.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • M. McIlhagga
    • 1
  1. 1.School of Cognitive and Computing SciencesUniversity of SussexFalmer Brighton

Personalised recommendations