Neural networks for process scheduling in real-time communication systems

This paper presents the use of Hopfield-type neural networks for process scheduling in the area of factory automation, where bus-based communication systems, called FieldBuses, are widely used to connect sensors and actuators to the control systems. We show how it overcomes the problem of the computational complexity of the algorithmic solution. The neural model proposed allows several processes to be scheduled simultaneously; the time required is polynomial with respect to the number of processes being scheduled. This feature allows real-time process scheduling and makes it possible for the scheduling table to adapt to changes in process control features. The paper presents the neural model for process scheduling and assesses its computational complexity, pointing out the drastic reduction in the time needed to generate a schedule as compared with the algorithmic scheduling solution. Finally, the authors propose an on-line scheduling strategy based on the neural model which can achieve real-time adaptation of the scheduling table to changes in the manufacturing environment.

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