Research Output

Scheduling chicken catching - an investigation into the success of a genetic algorithm on a real world scheduling problem.

  Genetic Algorithms (GAs) are a class of evolutionary algorithms that have been successfully
applied to scheduling problems, in particular job-shop and flow-shop type problems
where a number of theoretical benchmarks exist. This work applies a genetic algorithm to
a real-world, heavily constrained scheduling problem of a local chicken factory, where there
is no benchmark solution, but real-life needs to produce sensible and adaptable schedules in
a short space of time. The results show that the GA can successfully produce daily schedules
in minutes, similar to those currently produced by hand by a single expert in several days,
and furthermore improve certain aspects of the current schedules. We explore the success of
using a GA to evolve a strategy for producing a solution, rather than evolving the solution
itself, and find that this method provides the most flexible approach. This method can produce
robust schedules for all the cases presented to it. The algorithm itself is a compromise
between an indirect and direct representation. We conclude with a discussion on the suitability
of the genetic algorithm as an approach to this type of problem

  • Type:

    Article

  • Date:

    30 November 1998

  • Publication Status:

    Published

  • Publisher

    Springer

  • DOI:

    10.1023/A:1018951218434

  • ISSN:

    0254-5330

  • Library of Congress:

    QA75 Electronic computers. Computer science

  • Dewey Decimal Classification:

    006.3 Artificial intelligence

Citation

Hart, E., Ross, P. & Nelson, J. (1998). Scheduling chicken catching - an investigation into the success of a genetic algorithm on a real world scheduling problem. Annals of Operations Research. 92, 363-380. doi:10.1023/A:1018951218434. ISSN 0254-5330

Authors

Keywords

genetic algorithms; evolutionary algorithms; real world scheduling problems; job-shop; flow-shop; robust; flexibility; evolving heuristic strategy;

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