Evolving control rules for a dual-constrained job scheduling scenario
Created by W.Langdon from
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- @InProceedings{Branke:2016:WSC,
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author = "Juergen Branke and Matthew J. Groves and
Torsten Hildebrandt",
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booktitle = "2016 Winter Simulation Conference (WSC)",
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title = "Evolving control rules for a dual-constrained job
scheduling scenario",
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year = "2016",
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pages = "2568--2579",
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abstract = "Dispatching rules are often used for scheduling in
semiconductor manufacturing due to the complexity and
stochasticity of the problem. In the past,
simulation-based Genetic Programming has been shown to
be a powerful tool to automate the time-consuming and
expensive process of designing such rules. However, the
scheduling problems considered were usually only
constrained by the capacity of the machines. In this
paper, we extend this idea to dual-constrained flow
shop scheduling, with machines and operators for
loading and unloading to be scheduled simultaneously.
We show empirically on a small test problem with
parallel workstations, re-entrant flows and dynamic
stochastic job arrival that the approach is able to
generate dispatching rules that perform significantly
better than benchmark rules from the literature.",
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keywords = "genetic algorithms, genetic programming",
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DOI = "doi:10.1109/WSC.2016.7822295",
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month = dec,
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notes = "Also known as \cite{7822295}",
- }
Genetic Programming entries for
Jurgen Branke
Matthew J Groves
Torsten Hildebrandt
Citations