Using evolutionary algorithms and simulation for the optimization of manufacturing systems

This paper is concerned with an optimization method for manufacturing systems. This method is able to optimize problems with any type of variables (variables from a real set e.g. conveyor speed, an integer set e.g. size of buffer or any general set e.g. dispatching rules). It is based on the association of an evolutionary algorithm (EA) and a simulation model. Extensions of Michalewicz's genetic operators and algorithm (1992) are proposed to tackle manufacturing system problems. This method is applied to an example: the configuration of a workshop producing plastic yoghurt pots. The criterion to optimize is the cost of the workshop and the three variables are the size of a silo, the size of a warehouse and a choice between two manufacturing methods. The application has been realized by connecting a modified version of Michalewicz's Genocop software (for EA) and a simulation language.