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A multi-class teaching-learning-based optimization for multi-objective distributed hybrid flow shop scheduling
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DOI：10.1016/j.knosys.2023.110252.png)
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Distributed hybrid flow shop scheduling problem (DHFSP) has attracted some attention. In this study, DHFSP with sequence-dependent setup times is studied and a multi-class teaching-learning-based optimization (MTLBO) is proposed to minimize makespan and maximum tardiness simultaneously. A two-string representation is adopted. s classes are formed to improve search efficiency by implementing reward and punishment mechanism among them. Class evaluation is introduced and two teacher phases and one learner phase are applied in the evolution of each class. Elimination process acts on the worst class to avoid the waste of computing resource. A number of experiments are conducted and the computational results demonstrate that MTLBO is a very competitive method for DHFSP. (c) 2023 Published by Elsevier B.V.
Keyword:
Hybrid flow shop
Distributed scheduling
Multiple classes
Teaching-learning-based optimization
Reward and punishment mechanism
期刊
K
IF:
7.6
论文数:
1.2W
被引数:
4.5W
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