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

Tan, Choo Jun, Neoh, Siew Chin, Lim, Chee Peng, Hanoun, Samer, Wong, Wai Peng, Loo, Chu Kong, Zhang, Li and Nahavandi, Saeid 2017, Application of an evolutionary algorithm-based ensemble model to job-shop scheduling, Journal of intelligent manufacturing, pp. 1-12, doi: 10.1007/s10845-016-1291-1.

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Title Application of an evolutionary algorithm-based ensemble model to job-shop scheduling
Author(s) Tan, Choo Jun
Neoh, Siew Chin
Lim, Chee PengORCID iD for Lim, Chee Peng orcid.org/0000-0003-4191-9083
Hanoun, Samer
Wong, Wai Peng
Loo, Chu Kong
Zhang, Li
Nahavandi, Saeid
Journal name Journal of intelligent manufacturing
Start page 1
End page 12
Total pages 12
Publisher Springer
Place of publication Berlin, Germany
Publication date 2017-01-05
ISSN 0956-5515
1572-8145
Keyword(s) Multi-objective optimisation
Evolutionary algorithm
Ensemble model
Job-shop scheduling
Summary In this paper, a novel evolutionary algorithm is applied to tackle job-shop scheduling tasks in manufacturing environments. Specifically, a modified micro genetic algorithm (MmGA) is used as the building block to formulate an ensemble model to undertake multi-objective optimisation problems in job-shop scheduling. The MmGA ensemble is able to approximate the optimal solution under the Pareto optimality principle. To evaluate the effectiveness of the MmGA ensemble, a case study based on real requirements is conducted. The results positively indicate the effectiveness of the MmGA ensemble in undertaking job-shop scheduling problems.
Notes In Press
Language eng
DOI 10.1007/s10845-016-1291-1
Field of Research 099999 Engineering not elsewhere classified
0910 Manufacturing Engineering
0801 Artificial Intelligence And Image Processing
0899 Other Information And Computing Sciences
Socio Economic Objective 970110 Expanding Knowledge in Technology
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2016, Springer
Persistent URL http://hdl.handle.net/10536/DRO/DU:30090877

Document type: Journal Article
Collection: Centre for Intelligent Systems Research
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