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 b een successfully applied to scheduling problems, in particular job-shop and flow-shop type problems where a number of theoretical benchmarks exist. Th is work applies a genetic algorithm to a real-world, heavily constrained sc heduling problem of a local chicken factory, where there is no benchmark so lution, but real-life needs to produce sensible and adaptable schedules in a short space of time. The results show that the GA can successfully produc e daily schedules in minutes, similar to those currently produced by hand b y 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 metho d can produce robust schedules for all the cases presented to it. The algor ithm itself is a compromise between an indirect and direct representation. We conclude with a discussion on the suitability of the genetic algorithm a s an approach to this type of problem.