Learning iterative dispatching rules for job shop scheduling with genetic programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @Article{Nguyen:2013:ijamt,
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author = "Su Nguyen and Mengjie Zhang and Mark Johnston and
Kay Chen Tan",
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title = "Learning iterative dispatching rules for job shop
scheduling with genetic programming",
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journal = "The International Journal of Advanced Manufacturing
Technology",
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year = "2013",
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volume = "67",
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number = "1-4",
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pages = "85--100",
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month = jul,
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keywords = "genetic algorithms, genetic programming, Job shop,
Dispatching rule, Local search",
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ISSN = "0268-3768",
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DOI = "doi:10.1007/s00170-013-4756-9",
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abstract = "This study proposes a new type of dispatching rule for
job shop scheduling problems. The novelty of these
dispatching rules is that they can iteratively improve
the schedules by using the information from completed
schedules. While the quality of the schedule can be
improved, the proposed iterative dispatching rules
(IDRs) still maintain the easiness of implementation
and low computational effort of the traditional
dispatching rules. This feature makes them more
attractive for large-scale manufacturing systems. A
genetic programming (GP) method is developed in this
paper to evolve IDRs for job shop scheduling problems.
The results show that the proposed GP method is
significantly better than the simple GP method for
evolving composite dispatching rules. The evolved IDRs
also show their superiority to the benchmark
dispatching rules when tested on different problem
instances with makespan and total weighted tardiness as
the objectives. Different aspects of IDRs are also
investigated and the insights from these analyses are
used to enhance the performance of IDRs.",
-
language = "English",
- }
Genetic Programming entries for
Su Nguyen
Mengjie Zhang
Mark Johnston
Kay Chen Tan
Citations