INTEGRATION OF NEURAL NETWORKS AND GENETIC ALGORITHMS FOR AN INTELLIGENT MANUFACTURING CONTROLLER

This paper addresses the development and implementation of a ''control ler'' for a single manufacturing machine. This prototype will serve as an important tool to study the integration of several functions and t he utilization of status data to evaluate scheduling and control decis ion 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 capabil ity is implemented by using neural networks, simulation, and generic a lgorithms. Neural networks predict the behavior of different sequencin g policies available in the system. The contribution of the genetic al gorithms to the decision-making process is the development of a ''new' ' scheduling rule based on a ''building blocks'' procedure initiated b y the neural networks.