Intelligent scheduling for flexible manufacturing systems

A scheme for the scheduling of flexible manufacturing systems (FMSs) have been developed. It integrates neural networks, parallel Monte-Carlo simulation, genetic algorithms, and machine learning. Modular neural networks are used to generate a small set of attractive plans and schedules from a larger list of such plans and schedules. Parallel Monte-Carlo simulation predicts the impact of each on the future evolution of the manufacturing system. Genetic algorithms are utilized to combine attractive alternatives into a single best decision. Induction mechanisms are used for learning and simplify the decision process for future performance. The development of a modular neural network architecture for candidate rule selection for a FMS cell is investigated. A scheduling example illustrates the scheme capabilities including speed, adaptability, and computational efficiency