Genetic algorithms for the setup coordination in consecutive stages of a production chain

This paper considers the use of multi-objective genetic algorithms for solving a typical production chain problem, in which two consecutive production stages have to schedule their internal work while taking into account each other’s requirements. We focus on a multi-objective genetic algorithm recently proposed in the related literature, i.e. IGA (Intelligent Genetic Algorithm), comparing the solutions it yields with those obtained by two state-of-the-art genetic optimizers. A set of preliminary computational tests on the mentioned case study using industrial data indicate that IGA is a promising multi objective optimizer for typical supply chain planning and scheduling problems