Evolving dispatching rules for dynamic Job shop scheduling with uncertain processing times
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{karunakaran:2017:CEC,
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author = "Deepak Karunakaran and Yi Mei and Gang Chen2 and
Mengjie Zhang",
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booktitle = "2017 IEEE Congress on Evolutionary Computation (CEC)",
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title = "Evolving dispatching rules for dynamic Job shop
scheduling with uncertain processing times",
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year = "2017",
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editor = "Jose A. Lozano",
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pages = "364--371",
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address = "Donostia, San Sebastian, Spain",
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publisher = "IEEE",
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isbn13 = "978-1-5090-4601-0",
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abstract = "Dynamic Job shop scheduling (DJSS) is a complex and
hard problem in real-world manufacturing systems. In
practice, the parameters of a job shop like processing
times, due dates, etc. are uncertain. But most of the
current research on scheduling consider only
deterministic scenarios. In a typical dynamic job shop,
once the information about a job becomes available it
is considered unchanged. In this work, we consider
genetic programming based dispatching rules to generate
schedules in an uncertain environment where the process
time of an operation is not known exactly until it is
finished. Our primary goal is to investigate methods to
incorporate the uncertainty information into the
dispatching rules. We develop two training approaches,
namely ex-post and ex-ante to evolve the dispatching
rules to generate good schedules under uncertainty.
Both these methods consider different ways of
incorporating the uncertainty parameters into the
genetic programs during evolution. We test our methods
under different scenarios and the results compare well
against the existing approaches. We also test the
generalization capability of our methods across
different levels of uncertainty and observe that the
proposed methods perform well. In particular, we
observe that the proposed ex-ante training approach
outperformed other methods.",
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keywords = "genetic algorithms, genetic programming, dispatching,
job shop scheduling, manufacturing systems, dynamic job
shop scheduling, ex-ante training, ex-post training,
generalization capability, genetic programming based
dispatching rules, training approaches, uncertain
environment, uncertainty information, uncertainty
parameters, Dynamic scheduling, Optimization,
Schedules, Training, Uncertainty",
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isbn13 = "978-1-5090-4601-0",
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DOI = "doi:10.1109/CEC.2017.7969335",
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month = "5-8 " # jun,
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notes = "IEEE Catalog Number: CFP17ICE-ART Also known as
\cite{7969335}",
- }
Genetic Programming entries for
Deepak Karunakaran
Yi Mei
Aaron Chen
Mengjie Zhang
Citations