Petri nets and genetic algorithms to increase productivity in FMS

Full exploitation of available resources in a flexible manufacturing system is extremely important to optimize its productivity. The parameters on which to act in order to maximize exploitation of the resources are the sequence of activities performed by each resource and the number of parts processed in each production cycle (WIP). The aim of the paper is to propose a performance optimization strategy which takes both these parameters into account. The strategy is based on a genetic algorithm, whose aim is to explore as far as possible sequences of activities performed by each resource. For each sequence, a heuristic algorithm, the adjustment heuristic algorithm, is applied to find the minimum WIP. In this way, the sequence scenario which offers the minimum WIP is found