Abstract
Genetic Algorithms (GAs) are a class of evolutionary algorithms that havebeen successfullyapplied to scheduling problems, in particular job‐shop and flow‐shop type problemswhere a number of theoretical benchmarks exist. This work applies a genetic algorithm toa real‐world, heavily constrained scheduling problem of a local chicken factory, wherethere is no benchmark solution, but real‐life needs to produce sensible and adaptableschedules in a short space of time. The results show that the GA can successfully producedaily schedules in minutes, similar to those currently produced by hand by a single expertin several days, and furthermore improve certain aspects of the current schedules. Weexplore the success ofusing a GA to evolve a strategy for producing a solution, rather than evolving the solutionitself, and find that this method provides the most flexible approach. This method canproduce robust schedules for all the cases presented to it. The algorithm itself is acompromise between an indirect and direct representation. We conclude with a discussion onthe suitability of the genetic algorithm as an approach to this type of problem.
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Hart, E., Ross, P. & Nelson, J. Scheduling chicken catching ‐ An investigationinto the success of a genetic algorithm on areal‐world scheduling problem. Annals of Operations Research 92, 363–380 (1999). https://doi.org/10.1023/A:1018951218434
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DOI: https://doi.org/10.1023/A:1018951218434