Handling precedence constraints with neural network based real-time scheduling algorithms

In previous work, the authors proposed an approach to the approximate solution of scheduling problems, neural network based algorithms, applied to the preemptive and non-preemptive scheduling for a mono or multiprocessor environment. Results were presented in a systematic approach for translating task constraints into neural network building rules that are independently added to the neural architecture. The main advantage of this methodology is that the neural network built according the rules converges to a solution of the scheduling problem in only a few propagation times of analogue amplifiers. They present new rules that extend the methodology to handle precedence constraints. They present the formal energy function which occurs when the precedence constraints are met and finally present a performance analysis of the quality of the results obtained by this approach.