This paper presents the use of 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, The need to respect the strong time constraints typical of the applications supported in these systems requires suitable scheduling strategies in order to devise an appropriate sequence for transmission of the information produced by the processes using the communication system, The most widely used strategy consists of drawing up off-line scheduling tables containing this transmission sequence. As the size of a table may be considerable, thus creating serious problems for memorization, the authors previously developed an innovative method which allows the size of the table to be reduced greatly but complicates the problem of scheduling as it requires the use of computationally complex algorithms, in this paper, they present an alternative approach to scheduling based on a Hopfield-type neural-network model and 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.
|Titolo:||Neural networks for process scheduling in real-time communication systems|
|Autori interni:||MIRABELLA, Orazio|
|Data di pubblicazione:||1996|
|Rivista:||IEEE TRANSACTIONS ON NEURAL NETWORKS|
|Appare nelle tipologie:||1.1 Articolo in rivista|