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Investigating parallel genetic algorithms on job shop scheduling problems

  • Evolution and NP-Hard Problems
  • Conference paper
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Evolutionary Programming VI (EP 1997)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1213))

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Abstract

This paper describes a GA for job shop scheduling problems. Using the Giffler and Thompson algorithm, we created two new operators, THX crossover and mutation, which better transmit temporal relationships in the schedule. The approach produced excellent results on standard benchmark job shop scheduling problems. We further tested many models and scales of parallel GAs in the context of job shop scheduling problems. In our experiments, the hybrid model consisting of coarse-grain GAs connected in a fine-grain-GA-style topology performed best, appearing to integrate successfully the advantages of coarse-grain and fine-grain GAs.

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Author information

Authors and Affiliations

  1. Genetic Algorithms Research and Applications Group, Michigan State University, 48823, East Lansing, MI

    Shyh-Chang Lin, Erik D. Goodman & William F. Punch III

Authors
  1. Shyh-Chang Lin
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  2. Erik D. Goodman
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  3. William F. Punch III
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Editor information

Peter J. Angeline Robert G. Reynolds John R. McDonnell Russ Eberhart

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© 1997 Springer-Verlag Berlin Heidelberg

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Lin, SC., Goodman, E.D., Punch, W.F. (1997). Investigating parallel genetic algorithms on job shop scheduling problems. In: Angeline, P.J., Reynolds, R.G., McDonnell, J.R., Eberhart, R. (eds) Evolutionary Programming VI. EP 1997. Lecture Notes in Computer Science, vol 1213. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0014827

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  • DOI: https://doi.org/10.1007/BFb0014827

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-62788-3

  • Online ISBN: 978-3-540-68518-0

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