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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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 13:25
Last Modified: 17 Sep 2013 14:49

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