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An application of the neural network energy function to machine sequencing

Author

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  • Komgrit Leksakul
  • Anulark Techanitisawad

Abstract

We apply a neural network approach for solving a one-machine sequencing problem to minimize either single- or multi-objectives, namely the total tardiness, total flowtime, maximimum tardiness, maximum flowtime, and number of tardy jobs. We formulate correspondingly nonlinear integer models, for each of which we derive a quadratic energy function, a neural network, and a system of differential equations. Simulation results based on solving the nonlinear differential equations demonstrate that our approach can effectively solve the sequencing problems to optimality in most cases and near optimality in a few cases. The neural network approach can also be implemented on a parallel computing network, resulting in significant runtime savings over the optimization approach. Copyright Springer-Verlag Berlin/Heidelberg 2005

Suggested Citation

  • Komgrit Leksakul & Anulark Techanitisawad, 2005. "An application of the neural network energy function to machine sequencing," Computational Management Science, Springer, vol. 4(4), pages 309-338, November.
    • Handle: RePEc:spr:comgts:v:4:y:2005:i:4:p:309-338
    DOI: 10.1007/s10287-005-0037-x
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    Cited by:

    1. Mina Roohnavazfar & Daniele Manerba & Lohic Fotio Tiotsop & Seyed Hamid Reza Pasandideh & Roberto Tadei, 2021. "Stochastic single machine scheduling problem as a multi-stage dynamic random decision process," Computational Management Science, Springer, vol. 18(3), pages 267-297, July.
    2. Songsong Liu & Jose Pinto & Lazaros Papageorgiou, 2010. "MILP-based approaches for medium-term planning of single-stage continuous multiproduct plants with parallel units," Computational Management Science, Springer, vol. 7(4), pages 407-435, October.

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