Anytime scheduling with neural networks

Scheduling techniques have been intensively studied by several research communities and have been applied to a wide range of applications in computer and manufacturing environments. In computer systems, scheduling is an important approach to address real-time constraints associated with a set of computing tasks to be executed on one or several computers. Most of the scheduling problems are NP-hard, which is why heuristic and approximation algorithms must be used for large problems. Obviously these methods are of interest when they provide near optimal solutions with a polynomial computational complexity. This paper presents results for scheduling a set of nonpreemptive tasks by using a Hopfield neural network model. We present in this paper how this approach can solve scheduling problems following the “anytime” paradigm