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Neural networks for process scheduling in real-time communication systems
1Fac. di Ingegneria Istituto di Inf. e Telecomunicazioni, Catania Univ.
IEEE Transactions on Neural Networks
|January 1, 1996
Summary
This study introduces Hopfield-type neural networks for efficient factory automation process scheduling. This approach significantly reduces computation time and enables real-time adaptation in manufacturing environments.
Area of Science:
- Factory automation
- Industrial control systems
- Computational complexity
Background:
- FieldBuses are crucial for connecting sensors and actuators in factory automation.
- Algorithmic solutions for process scheduling face computational complexity challenges.
- Real-time adaptation is essential for dynamic manufacturing environments.
Purpose of the Study:
- To present a Hopfield-type neural network model for process scheduling in factory automation.
- To demonstrate the model's ability to overcome computational complexity issues.
- To enable real-time adaptation of scheduling tables in manufacturing.
Main Methods:
- Utilizing Hopfield-type neural networks for simultaneous process scheduling.
- Analyzing the polynomial time complexity with respect to the number of processes.
- Developing an on-line scheduling strategy based on the neural model.
Main Results:
- The neural model significantly reduces the time required for schedule generation compared to algorithmic solutions.
- The proposed model allows for simultaneous scheduling of multiple processes.
- Achieved polynomial time complexity, enabling real-time process scheduling.
Conclusions:
- Hopfield-type neural networks offer an efficient solution for process scheduling in factory automation.
- The neural model provides a drastic reduction in computational time and enables real-time adaptability.
- The on-line scheduling strategy facilitates dynamic adjustments to the manufacturing environment.
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