An ant colony optimization-based hyper-heuristic with genetic programming approach for a hybrid flow shop scheduling problem
Created by W.Langdon from
gp-bibliography.bib Revision:1.7975
- @InProceedings{Chen:2015:CECb,
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author = "Lin Chen and Hong Zheng and Dan Zheng and Dongni Li",
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booktitle = "2015 IEEE Congress on Evolutionary Computation (CEC)",
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title = "An ant colony optimization-based hyper-heuristic with
genetic programming approach for a hybrid flow shop
scheduling problem",
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year = "2015",
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pages = "814--821",
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isbn13 = "978-1-4799-7491-7",
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abstract = "The problem of a k-stage hybrid flow shop (HFS) with
one stage composed of non-identical batch processing
machines and the others consisting of non-identical
single processing machines is analysed in the context
of the equipment manufacturing industry. Due to the
complexity of the addressed problem, a hyper-heuristic
which combines heuristic generation and heuristic
search is proposed to solve the problem. For each
sub-problem, i.e., part assignment, part sequencing and
batch formation, heuristic rules are first generated by
genetic programming (GP) off-line and then selected by
ant colony optimisation (ACO) correspondingly. Finally,
the scheduling solutions are obtained through the above
generated combinatorial heuristic rules. Aiming at
minimizing the total weighted tardiness of parts, a
comparison experiment with the other hyper-heuristic
for the same HFS problem is conducted. The result has
shown that the proposed algorithm has advantages over
the other method with respect to the total weighted
tardiness.",
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keywords = "genetic algorithms, genetic programming, ant colony
optimization, ACO, scheduling, discrete event systems",
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DOI = "doi:10.1109/CEC.2015.7256975",
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ISSN = "1089-778X",
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month = may,
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notes = "Also known as \cite{7256975}",
- }
Genetic Programming entries for
Lin Chen
Hong Zheng
Dan Zheng
Dongni Li
Citations