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

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

来源：Journal of Intelligent Manufacturing, 2019, 30(2): 879-890.

DOI：10.1007/s10845-016-1291-1

• Multi-objective optimisation
• Evolutionary algorithm
• Ensemble model
• Job-shop scheduling

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.

• 出版日期2019-2
• 单位迪肯大学