Computationally efficient neuro-dynamic programming approximation method for the capacitated re-entrant line scheduling problem
- Authors
- Choi, Jin Young; Kim, Seoung Bum
- Issue Date
- 2012
- Publisher
- TAYLOR & FRANCIS LTD
- Keywords
- capacitated re-entrant line; data mining; feature selection; scheduling; neuro-dynamic programming; principal component analysis
- Citation
- INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH, v.50, no.8, pp.2353 - 2362
- Indexed
- SCIE
SCOPUS
- Journal Title
- INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
- Volume
- 50
- Number
- 8
- Start Page
- 2353
- End Page
- 2362
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/109387
- DOI
- 10.1080/00207543.2011.578596
- ISSN
- 0020-7543
- 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.
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Collections - College of Engineering > School of Industrial and Management Engineering > 1. Journal Articles
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