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A Constraint Satisfaction Adaptive Neural Network with Dynamic Model for Job-Shop Scheduling Problem

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Advances in Neural Networks - ISNN 2006 (ISNN 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3973))

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Abstract

It is well known, the Job-Shop Scheduling Problem (JSSP) is the most complicated and typical problem of all kinds of production scheduling problems, the allocation of resources over time to perform a collection of tasks. The current method has several shortcomings in solving the JSSP. In this paper, we correct these deficiencies by introducing a dynamic model that is based on an analysis of the run-time behavior of CSANN algorithm. At the same time, this paper proposes several new heuristics in order to improve the performance of CSANN. The computational simulations have shown that the proposed hybrid approach has good performance with respect to the quality of solution and the speed of computation.

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References

  1. Baker, K.R.: Introduction to Sequence and Scheduling. Wiley, New York (1974)

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  2. Garey, M.R., Johnson, D.S., Sethi, R.: The Complexity of Flow-Shop and Job-Shop Scheduling. Mathematics of Operations Research 1(2), 117–129 (1976)

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Author information

Authors and Affiliations

  1. Department of Management, School of Information System and Management, National University of Defense Technology, Changsha, 410073, P.R. China

    Li-Ning Xing, Ying-Wu Chen & Xue-Shi Shen

Authors
  1. Li-Ning Xing
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  2. Ying-Wu Chen
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  3. Xue-Shi Shen
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Editor information

Editors and Affiliations

  1. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

    Jun Wang

  2. Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, P.R. China

    Zhang Yi

  3. Department of Electrical Engineering, University of Louisville, 40292, Louisville, KY, U.S.A

    Jacek M. Zurada

  4. Laboratory for Computational Biology, Shanghai Center for Systems Biomedicine, 800 Dong Chuan Rd, 200240, Shanghai, China

    Bao-Liang Lu

  5. School of Electrical and Electronic Engineering, University of Manchester, UK

    Hujun Yin

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© 2006 Springer-Verlag Berlin Heidelberg

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Xing, LN., Chen, YW., Shen, XS. (2006). A Constraint Satisfaction Adaptive Neural Network with Dynamic Model for Job-Shop Scheduling Problem. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3973. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760191_135

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  • DOI: https://doi.org/10.1007/11760191_135

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-34482-7

  • Online ISBN: 978-3-540-34483-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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