On the Use of an Enhanced Hopfield Neural Model to Solve FMS Performance Optimization Problem

摘要

The performance of a Flexible Manufacturing System (FMS) is generally linked to its productivity and is often limited by poor use of available resources. One of the main goals in the automated factory environment is, therefore, the exploitation of resources to the full, in such a way as to optimize productivity. 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 algorithms which are different from 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 to a network that is well known in literature: the Hopfield network. The solution proposed is an original one and features several advantages against the most known heuristic approaches to the problem, the most important of which is the possibility of optimal or close to optimal solutions in a polynomial time, proportional to the size of the FMS. In addition, the possibility of simple, economical hardware implementation of the neural model favours its integration in the automated factory environment, allowing real-time supervision and optimization of productivity. The aim of the paper is to present the new neural model and its use in optimizing the performance of FMSs. A comparison of the neural approach with classical heuristic solutions and its real-time calculation capability, will also be treated in the paper.