Automatic design of scheduling policies for dynamic flexible job shop scheduling via surrogate-assisted cooperative co-evolution genetic programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @Article{Zhou:2020:IJPR,
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author = "Yong Zhou and Jian-jun Yang and Zhuang Huang",
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title = "Automatic design of scheduling policies for dynamic
flexible job shop scheduling via surrogate-assisted
cooperative co-evolution genetic programming",
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journal = "International Journal of Production Research",
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year = "2020",
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volume = "58",
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number = "9",
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pages = "2561--2580",
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keywords = "genetic algorithms, genetic programming",
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ISSN = "0020-7543",
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bibsource = "OAI-PMH server at oai.repec.org",
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identifier = "RePEc:taf:tprsxx:v:58:y:2020:i:9:p:2561-2580",
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oai = "oai:RePEc:taf:tprsxx:v:58:y:2020:i:9:p:2561-2580",
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DOI = "doi:10.1080/00207543.2019.1620362",
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abstract = "At present, a lot of references use discrete event
simulation to evaluate the fitness of evolved rules,
but which simulation configuration can achieve better
evolutionary rules in a limited time has not been fully
studied. This study proposes three types of
hyper-heuristic methods for coevolution of the machine
assignment rules (MAR) and job sequencing rules (JSR)
to solve the DFJSP, including the cooperative
coevolution genetic programming with two
sub-populations (CCGP), the genetic programming with
two sub-trees (TTGP) and the genetic expression
programming with two sub-chromosomes (GEP). After
careful parameter tuning, a surrogate simulation model
is used to evaluate the fitness of evolved scheduling
policies (SP). Computational simulations and
comparisons demonstrate that the proposed
surrogate-assisted CCGP method (CCGP-SM) shows
competitive performance with other evolutionary
approaches using the same computation time.
Furthermore, the learning process of the proposed
methods demonstrates that the surrogate-assisted GP
methods help accelerating the evolutionary process and
improving the quality of the evolved SPs without a
significant increase in the length of SP. In addition,
the evolved SPs generated by the CCGP-SM show superior
performance as compared with existing rules in the
literature. These results demonstrate the effectiveness
and robustness of the proposed method.",
- }
Genetic Programming entries for
Yong Zhou
Jian-Jun Yang
Zhuang Huang
Citations