Application of an evolutionary algorithm-based ensemble model to 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-s...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Published: |
Springer Verlag
2019
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| Subjects: | |
| Online Access: | http://eprints.um.edu.my/23319/ https://doi.org/10.1007/s10845-016-1291-1 |
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| Summary: | 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. |
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