Stochastic Approximate Scheduling by Neurodynamic Learning

The paper suggests a stochastic approximate solution to scheduling problems with unrelated parallel machines. The presented method is based on neurodynamic programming (reinforcement learning and feed-forward artificial neural networks). For various scheduling environments (static-dynamic, deterministicstochastic) different variants of episodic Q-learning rules are proposed. A way to improve the avoidance of local minima is also discussed. Some investigations on the exploration strategy, function approximation and parallelizing the solution are made. Finally, a few experimental results are shown. Copyright c © 2005 IFAC