Evolutionary generation of dispatching rule sets for complex dynamic scheduling problems
Created by W.Langdon from
gp-bibliography.bib Revision:1.7970
- @Article{Pickardt:2012:IJPE,
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author = "Christoph W. Pickardt and Torsten Hildebrandt and
Jurgen Branke and Jens Heger and Bernd Scholz-Reiter",
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title = "Evolutionary generation of dispatching rule sets for
complex dynamic scheduling problems",
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journal = "International Journal of Production Economics",
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year = "2013",
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volume = "145",
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number = "1",
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pages = "67--77",
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ISSN = "0925-5273",
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DOI = "doi:10.1016/j.ijpe.2012.10.016",
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URL = "http://www.sciencedirect.com/science/article/pii/S0925527312004574",
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abstract = "We propose a two-stage hyper-heuristic for the
generation of a set of work centre-specific dispatching
rules. The approach combines a genetic programming (GP)
algorithm that evolves a composite rule from basic job
attributes with an evolutionary algorithm (EA) that
searches for a good assignment of rules to work
centres. The hyper-heuristic is tested against its two
components and rules from the literature on a complex
dynamic job shop problem from semiconductor
manufacturing. Results show that all three
hyper-heuristics are able to generate (sets of) rules
that achieve a significantly lower mean weighted
tardiness than any of the benchmark rules. Moreover,
the two-stage approach proves to outperform the GP and
EA hyper-heuristic as it optimises on two different
heuristic search spaces that appear to tap different
optimisation potentials. The resulting rule sets are
also robust to most changes in the operating
conditions.",
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keywords = "genetic algorithms, genetic programming,
Hyper-heuristics, Dispatching rules, Production
scheduling, Semiconductor manufacturing, Evolutionary
algorithms",
- }
Genetic Programming entries for
Christoph Pickardt
Torsten Hildebrandt
Jurgen Branke
Jens Heger
Bernd Scholz-Reiter
Citations