Solving Inequality Constraints Job Scheduling Problem by Slack Competitive Neural Scheme

Abstract

A competitive neural network provides a highly effective means of attaining a sound solution and of reducing the network complexity. A competitive approach is utilized to deal with fully-utilized scheduling problems. This investigation employs slack competitive Hopfield neural network (SCHNN) to resolve non-fully and fully utilized identical machine scheduling problems with multi-constraint, real time (execution time and deadline constraints) and resource constraints. To facilitate resolving the scheduling problems, extra slack neurons are added on to the neural networks to represent pseudo-jobs. This study presents an energy function corresponding to a neural network containing slack neurons. Simulation results demonstrate that the proposed energy function integrating competitive neural network with slack neurons can solve fully and non-fully utilized real-time scheduling problems.