Application of an evolutionary algorithm-based ensemble model to job-shop scheduling-Reference-Cited by-同舟云学术

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Application of an evolutionary algorithm-based ensemble model to job-shop scheduling

Published:2017-01-05 Issue:2 Volume:30 Page:879-890
ISSN:0956-5515
Container-title:Journal of Intelligent Manufacturing
language:en
Short-container-title:J Intell Manuf

Author:

Tan Choo Jun,Neoh Siew Chin,Lim Chee Peng,Hanoun Samer,Wong Wai Peng,Loo Chu Kong,Zhang Li,Nahavandi Saeid

Funder

Collaborative Research in Engineering, Science, and Technology

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Industrial and Manufacturing Engineering,Software

Link

http://link.springer.com/article/10.1007/s10845-016-1291-1/fulltext.html

Reference79 articles.

1. Abu-Lebdeh, G., & Benekohal, R. F. (1999). Convergence variability and population sizing in micro-genetic algorithms. Computer-Aided Civil and Infrastructure Engineering, 14(5), 321–334.

2. Amirian, H., & Sahraeian, R. (2015). Augmented $$\epsilon $$ ϵ -constraint method in multi-objective flowshop problem with past sequence set-up times and a modified learning effect. International Journal of Production Research, 53(19), 5962–5976.

3. Archimede, B., Letouzey, A., Memon, M., & Xu, J. (2014). Towards a distributed multi-agent framework for shared resources scheduling. Journal of Intelligent Manufacturing, 25(5), 1077–1087.

4. Bäck, T., Hammel, U., & Schwefel, H. P. (1997). Evolutionary computation: Comments on the history and current state. IEEE Transactions on Evolutionary Computation, 1(1), 3–17.

5. Bäck, T., & Schwefel, H. P. (1993). An overview of evolutionary algorithms for parameter optimization. Evolutionary Computation, 1(1), 1–23.

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