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 Kiong; Zhang, Li; Nahavandi, Saeid (Springer Verlag, 2019)
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.