Hyper-Heuristic Coevolution of Machine Assignment and Job Sequencing Rules for Multi-Objective Dynamic Flexible Job Shop Scheduling
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- @Article{Zhou:2019:IEEEAccess,
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author = "Yong Zhou and Jian-Jun Yang and Lian-Yu Zheng",
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journal = "IEEE Access",
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title = "Hyper-Heuristic Coevolution of Machine Assignment and
Job Sequencing Rules for Multi-Objective Dynamic
Flexible Job Shop Scheduling",
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year = "2019",
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volume = "7",
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pages = "68--88",
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keywords = "genetic algorithms, genetic programming, genetic
expression programming",
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DOI = "doi:10.1109/ACCESS.2018.2883802",
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ISSN = "2169-3536",
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abstract = "Nowadays, real-time scheduling is one of the key
issues in cyber-physical system. In real production,
dispatching rules are frequently used to react to
disruptions. However, the man-made rules have strong
problem relevance, and the quality of results depends
on the problem itself. The motivation of this paper is
to generate effective scheduling policies (SPs) through
off-line learning and to implement the evolved SPs
online for fast application. Thus, the dynamic
scheduling effectiveness can be achieved, and it will
save the cost of expertise and facilitate large-scale
applications. Three types of hyper-heuristic methods
were proposed in this paper for coevolution of the
machine assignment rules and job sequencing rules to
solve the multi-objective dynamic flexible job shop
scheduling problem, including the multi-objective
cooperative coevolution genetic programming with two
sub-populations, the multi-objective genetic
programming with two sub-trees, and the multi-objective
genetic expression programming with two chromosomes.
Both the training and testing results demonstrate that
the CCGP-NSGAII method is more competitive than other
evolutionary approaches. To investigate the
generalization performance of the evolved SPs, the
non-dominated SPs were applied to both the training and
testing scenarios to compare with the 320 types of
man-made SPs. The results reveal that the evolved SPs
can discover more useful heuristics and behave more
competitive than the man-made SPs in more complex
scheduling scenarios. It also demonstrates that the
evolved SPs have a strong generalization performance to
be reused in new unobserved scheduling scenarios.",
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notes = "Also known as \cite{8550675}",
- }
Genetic Programming entries for
Yong Zhou
Jian-Jun Yang
Lian-Yu Zheng
Citations