Scheduling multiprocessor jobs using a Hopfield neural network

Multiprocessor job scheduling is a complicated combinatorial optimization problem, and the Hopfield neural network is extensively applied to solve various combinatorial optimization problems. An effective Hopfield neural network (HNN) approach to a multiprocessor job scheduling problem (known to be an NP-hard problem) is proposed, which is suited to resource and timing (execution time and deadline) constraints. This approach directly formulates the energy function of HNN according to constraints term by term and derives an HNN model. Simulation results demonstrate that the derived energy function works effectively for this class of problems.