Real time scheduling with Neurosched

Most scheduling problems are NP hard. Therefore, heuristics and approximation algorithms must be used for large problems when timing constraints have to be addressed. Obviously these methods are of interest when they provide near optimal solutions and when computational complexity can be controlled. The paper presents such a method based on Hopfield neural networks. Scheduling problems are solved in an iterative way, by finding a solution through the minimization of an energy function. An interesting property of this approach is its capacity to trade-off quality for computation time. Indeed, the convergence speed of the minimization process can be tuned by adapting several parameters that influence the quality of the results.