Simulation of manufacturing models and its learning with artificial neural networks

Introduces some new faces of simulation for intelligent manufacturing systems. Not only the productive parameters are treated from a multi-level point of view but the economic indicators were calculated with global optimistic features. The general marketing aspects are considered as critical conditions. To solve several decision making problems of a complex manufacturing organization, the authors extract meaningful variables to observe by data analysis and construct an artificial neural net. The authors try to take some advantages of non-symbolic processing such that parallel treatment and its implementation guide toward a real-time system, learning capabilities also can be applied independently to problems.