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Heuristic Combined Artificial Neural Networks to Schedule Hybrid Flow Shop with Sequence Dependent Setup Times

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Advances in Neural Networks – ISNN 2005 (ISNN 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3496))

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Abstract

This paper addresses the problem of arranging jobs to machines in hybrid flow shop in which the setup times are dependent on job sequence. A new heuristic combined artificial neural network approach is proposed. The traditional Hopfield network formulation is modified upon theoretical analysis. Compared with the common used permutation matrix, the new construction needs fewer neurons, which makes it possible to solve large scale problems. The traditional Hopfield network running manner is also modified to make it more competitive with the proposed heuristic algorithm. The performance of the proposed algorithm is verified by randomly generated instances. Computational results of different size of data show that the proposed approach works better when compared to the individual heuristic with random initialization.

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Author information

Authors and Affiliations

  1. Department of Systems Engineering, Northeastern University, Shenyang, Liaoning, 110000, China

    Lixin Tang & Yanyan Zhang

Authors
  1. Lixin Tang
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  2. Yanyan Zhang
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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. The Key Laboratory of Optoelectric Technology & Systems, Ministry of Education, China

    Xiaofeng Liao

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

    Zhang Yi

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© 2005 Springer-Verlag Berlin Heidelberg

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Tang, L., Zhang, Y. (2005). Heuristic Combined Artificial Neural Networks to Schedule Hybrid Flow Shop with Sequence Dependent Setup Times. In: Wang, J., Liao, X., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3496. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427391_126

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  • DOI: https://doi.org/10.1007/11427391_126

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-25912-1

  • Online ISBN: 978-3-540-32065-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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