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. ISSN 0956-5515 (In Press)

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Official URL: http://dx.doi.org/10.1007/s10845-016-1291-1

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 Science and Digital Technologies
Depositing User: Becky Skoyles
Date Deposited: 25 Jan 2017 16:13
Last Modified: 11 May 2017 11:59
URI: http://nrl.northumbria.ac.uk/id/eprint/29333

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