Application of an evolutionary algorithm-based ensemble model to job-shop scheduling
Version 2 2024-06-06, 08:07Version 2 2024-06-06, 08:07
Version 1 2017-01-30, 12:30Version 1 2017-01-30, 12:30
journal contribution
posted on 2019-02-01, 00:00authored byC J Tan, S C Neoh, Chee Peng Lim, Samer HanounSamer Hanoun, W P Wong, C K Loo, L Zhang, Saeid Nahavandi
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.