Solving inequality constraints job scheduling problem by slack competitive neural scheme

Authors:
Ruey-Maw Chen;Shih-Tang Lo;Yueh-Min Huang
Affiliations:
Department of Computer Science and Information Engineering, National Chin-yi University of Technology, Taichung, Taiwan, ROC;Department of Engineering Science, National Cheng-Kung University, Tainan, Taiwan, ROC;Department of Engineering Science, National Cheng-Kung University, Tainan, Taiwan, ROC
Venue:
IEA/AIE'07 Proceedings of the 20th international conference on Industrial, engineering, and other applications of applied intelligent systems
Year:
2007

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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.