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.