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

Hart, Emma; Ross, Peter; Nelson, Jeremy

Abstract

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

Citation

Hart, E., Ross, P., & Nelson, J. (1999). 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. https://doi.org/10.1023/A%3A1018951218434

• Journal Article Type: Article
• Publication Date: 1999
• Deposit Date: Sep 3, 2010
• Print ISSN: 0254-5330
• Electronic ISSN: 1572-9338
• Publisher: BMC
• Peer Reviewed: Peer Reviewed
• Volume: 92
• Pages: 363-380
• Book Title: Annals of Operations Research
• DOI: https://doi.org/10.1023/A%3A1018951218434
• Keywords: genetic algorithms; evolutionary algorithms; real world scheduling problems; job-shop; flow-shop; robust; flexibility; evolving heuristic strategy
• Public URL: http://researchrepository.napier.ac.uk/id/eprint/3175
• Publisher URL: http://dx.doi.org/10.1023/A:1018951218434