A Hybrid Genetic Programming Algorithm for Automated Design of Dispatching Rules
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- @Article{Su_Nguyen:EC,
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author = "Su Nguyen and Yi Mei and Bing Xue and Mengjie Zhang",
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title = "A Hybrid Genetic Programming Algorithm for Automated
Design of Dispatching Rules",
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journal = "Evolutionary Computation",
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year = "2019",
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volume = "27",
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number = "3",
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pages = "467--496",
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month = "Fall",
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keywords = "genetic algorithms, genetic programming, job shop,
production scheduling, hyper-heuristics",
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ISSN = "1063-6560",
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URL = "https://homepages.ecs.vuw.ac.nz/~xuebing/Papers/ECJ18-Su.pdf",
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DOI = "doi:10.1162/evco_a_00230",
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size = "31 pages",
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abstract = "Designing effective dispatching rules for production
systems is a difficult and time consuming task if it is
done manually. In the last decade, the growth of
computing power, advanced machine learning, and
optimisation techniques has made the automated design
of dispatching rules possible and automatically
discovered rules are competitive or outperform existing
rules developed by researchers. Genetic programming is
one of the most popular approaches to discovering
dispatching rules in the literature, especially for
complex production systems. However, the large
heuristic search space may restrict genetic programming
from finding near optimal dispatching rules. This paper
develops a new hybrid genetic programming algorithm for
dynamic job shop scheduling based on a new
representation, a new local search heuristic, and
efficient fitness evaluators. Experiments show that the
new method is effective regarding the quality of
evolved rules. Moreover, evolved rules are also
significantly smaller and contain more relevant
attributes.",
- }
Genetic Programming entries for
Su Nguyen
Yi Mei
Bing Xue
Mengjie Zhang
Citations