A novel neural network model for the performance evaluation of flexible manufacturing systems

The paper deals with the problem of performance optimization of flexible manufacturing systems. As widely documented in literature, this is a hard task on account of its computational complexity. For this reason a number of heuristic techniques are currently available, the best known of which are based on event graphs, which are a particular class of Petri nets. The paper proposes a performance optimization technique which, although it is based on event graphs, applies different algorithms than traditional heuristic ones. More specifically, a novel neural model is used to solve the optimization problem. The neural model was obtained by making significant changes in a network which is well known in literature: the Hopfield network. The modifications were made in order to meet the constraints typical of performance optimization of flexible manufacturing systems. The aim of the paper is to present the new neural model and show the performance optimization results that can be obtained by using it. The results presented highlight the excellence of the solution proposed and its applicability in the factory automation environment.