Solving inequality constraints job scheduling problem by slack competitive neural scheme

Ruey Maw Chen, Shih Tang Lo, Yueh Min Huang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish
Title of host publicationNew Trends in Applied Artificial Intelligence - 20th International Conference on Industrial, Engineering, and Other Applications of Applied Intelligent Systems, lEA/AlE 2007, Proceedings
PublisherSpringer Verlag
Pages715-724
Number of pages10
ISBN (Print)9783540733225
DOIs
Publication statusPublished - 2007 Jan 1
Event20th International Conference on Industrial, Engineering, and Other Applications of Applied Intelligent Systems, lEA/AlE-2007 - Kyoto, Japan
Duration: 2007 Jun 262007 Jun 29

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4570 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other20th International Conference on Industrial, Engineering, and Other Applications of Applied Intelligent Systems, lEA/AlE-2007
CountryJapan
CityKyoto
Period07-06-2607-06-29

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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