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

Hart, Emma, Ross, Peter and Nelson, Jeremy (1999) Scheduling chicken catching - an investigation into the success of a genetic algorithm on a real world scheduling problem. Annals of Operations Research, 92. pp. 363-380. ISSN 0254-5330

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    Abstract/Description

    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

    Item Type: Article
    Print ISSN: 0254-5330
    Electronic ISSN: 1572-9338
    Uncontrolled Keywords: genetic algorithms; evolutionary algorithms; real world scheduling problems; job-shop; flow-shop; robust; flexibility; evolving heuristic strategy;
    University Divisions/Research Centres: Faculty of Engineering, Computing and Creative Industries > School of Computing
    Dewey Decimal Subjects: 000 Computer science, information & general works > 000 Computer science, knowledge & systems > 006 Special Computer Methods > 006.3 Artificial intelligence
    Library of Congress Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
    Item ID: 3175
    Depositing User: Computing Research
    Date Deposited: 03 Sep 2010 14:25
    Last Modified: 17 Sep 2013 15:49
    URI: http://researchrepository.napier.ac.uk/id/eprint/3175

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