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Computationally efficient neuro-dynamic programming approximation method for the capacitated re-entrant line scheduling problem
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DOI：10.1080/00207543.2011.578596.png)
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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.
Keyword:
capacitated re-entrant line
data mining
feature selection
scheduling
neuro-dynamic programming
principal component analysis
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IF:
7.3
论文数:
1.1W
被引数:
3.7W
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