Tan, Choo Jun, Neoh, Siew Chin, Lim, Chee Peng, Hanoun, Samer, Wong, Wai Peng, Loo, Chu Kong, 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
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Abstract
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.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Multi-objective optimisation, Evolutionary algorithm, Ensemble model, Job-shop scheduling |
| Subjects: | G400 Computer Science |
| Department: | Faculties > Engineering and Environment > Computer and Information Sciences |
| Depositing User: | Becky Skoyles |
| Date Deposited: | 25 Jan 2017 16:13 |
| Last Modified: | 01 Aug 2021 12:49 |
| URI: | http://nrl.northumbria.ac.uk/id/eprint/29333 |
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