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

Tan, Choo Jun and Neoh, Siew Chin and Lim, Chee Peng and Hanoun, Samer and Wong, Wai Peng and Loo, Chu Kiong and Zhang, Li and Nahavandi, Saeid (2019) Application of an evolutionary algorithm-based ensemble model to job-shop scheduling. Journal of Intelligent Manufacturing, 30 (2). pp. 879-890. ISSN 0956-5515, DOI

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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. © 2017, Springer Science+Business Media New York.

Item Type: Article
Funders: Collaborative Research in Engineering, Science and Technology (CREST) (Grant No. P05C2-14)
Uncontrolled Keywords: Ensemble model; Evolutionary algorithm; Job-shop scheduling; Multi-objective optimisation
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Computer Science & Information Technology
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 06 Jan 2020 03:20
Last Modified: 06 Jan 2020 03:20

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