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- W2092866163 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." @default.
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- W2092866163 date "2012-04-15" @default.
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- W2092866163 title "Computationally efficient neuro-dynamic programming approximation method for the capacitated re-entrant line scheduling problem" @default.
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- W2092866163 doi "https://doi.org/10.1080/00207543.2011.578596" @default.
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