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Multi-constraint System Scheduling Using Dynamic and Delay Ant Colony System

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New Trends in Applied Artificial Intelligence (IEA/AIE 2007)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4570))

Abstract

This study presents and evaluates a modified ant colony optimization (ACO) approach for the precedence and resource-constrained multiprocessor scheduling problems. A modified ant colony system, with two designed rules, called dynamic and delay ant colony system, is proposed to solve the scheduling problems. The dynamic rule is designed to modify the latest starting time of jobs and hence the heuristic function. A delay solution generation rule in exploration of the search solution space is used to escape the local optimal solution. Simulation results demonstrate that the proposed modified ant colony system algorithm provides an effective and efficient approach for solving multiprocessor system scheduling problems with precedence and resource constraints.

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Authors and Affiliations

  1. Department of Information Management, Kun-Shan University, Yung-Kang City, Tainan Hsien, 710, Taiwan, ROC

    Shih-Tang Lo

  2. Department of Computer Science and Information Engineering, National Chin-yi University of Technology, Taichung, 411, Taiwan, ROC

    Ruey-Maw Chen

  3. Department of Engineering Science, National Cheng-Kung University, Tainan, 701, Taiwan, ROC

    Yueh-Min Huang

Authors
  1. Shih-Tang Lo
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  2. Ruey-Maw Chen
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  3. Yueh-Min Huang
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Editor information

Hiroshi G. Okuno Moonis Ali

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© 2007 Springer Berlin Heidelberg

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Lo, ST., Chen, RM., Huang, YM. (2007). Multi-constraint System Scheduling Using Dynamic and Delay Ant Colony System. In: Okuno, H.G., Ali, M. (eds) New Trends in Applied Artificial Intelligence. IEA/AIE 2007. Lecture Notes in Computer Science(), vol 4570. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73325-6_65

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  • DOI: https://doi.org/10.1007/978-3-540-73325-6_65

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-73322-5

  • Online ISBN: 978-3-540-73325-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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