Integration of neural networks and genetic algorithms for an intelligent manufacturing controller

This paper addresses the development and implementation of a “controller” for a single manufacturing machine. This prototype will serve as an important tool to study the integration of several functions and the utilization of status data to evaluate scheduling and control decision alternatives. The emphasis is on creating a prediction capability to aid in assessing the long-term system performance impact resulting from decisions made and environmental changes. This prediction capability is implemented by using neural networks, simulation, and genetic algorithms. Neural networks predict the behavior of different sequencing policies available in the system. The contribution of the genetic algorithms to the decision-making process is the development of a “new” scheduling rule based on a “building blocks” procedure initiated by the neural networks