Computationally efficient neuro-dynamic programming approximation method for the capacitated re-entrant line scheduling problem

Jin Young Choi, Seoung Bum Kim

    Research output: Contribution to journalArticlepeer-review

    1 Citation (Scopus)

    Abstract

    This paper presents a computationally efficient neuro-dynamic programming approximation method for the capacitated re-entrant line scheduling problem by reducing the number of feature functions. The method is based on a statistical assessment of the significance of the various feature functions. This assessment can be made by combining the weighted principal components with a thresholding algorithm. The efficacy of the new feature functions selected is tested by numerical experiments. The results indicate that the feature selection method presented here can extract a small number of significant features with the potential capability of providing a compact representation of the target value function in a neuro-dynamic programming framework. Moreover, the linear parametric architecture considered holds considerable promise as a way to provide effective and computationally efficient approximations for an optimal scheduling policy that consistently outperforms the heuristics typically employed.

    Original languageEnglish
    Pages (from-to)2353-2362
    Number of pages10
    JournalInternational Journal of Production Research
    Volume50
    Issue number8
    DOIs
    Publication statusPublished - 2012 Apr 15

    Bibliographical note

    Funding Information:
    This work was supported by Grant Nos. 2009-0070818 and 2010-0003811 from the National Research Foundation of Korea and Brain Korea 21 (Network Enterprise).

    Keywords

    • capacitated re-entrant line
    • data mining
    • feature selection
    • neuro-dynamic programming
    • principal component analysis
    • scheduling

    ASJC Scopus subject areas

    • Strategy and Management
    • Management Science and Operations Research
    • Industrial and Manufacturing Engineering

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