Selection of dispatching rules evolved by genetic programming in dynamic unrelated machines scheduling based on problem characteristics
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- @Article{DURASEVIC2022101649,
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author = "Marko Durasevic and Domagoj Jakobovic",
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title = "Selection of dispatching rules evolved by genetic
programming in dynamic unrelated machines scheduling
based on problem characteristics",
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journal = "Journal of Computational Science",
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year = "2022",
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volume = "61",
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pages = "101649",
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month = may,
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keywords = "genetic algorithms, genetic programming, Dispatching
rules, Genetic programming, Scheduling, Unrelated
machines environment, Machine learning, Dispatching
rule selection",
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ISSN = "1877-7503",
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URL = "https://www.sciencedirect.com/science/article/pii/S1877750322000667",
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DOI = "doi:10.1016/j.jocs.2022.101649",
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abstract = "Dispatching rules are fast and simple procedures for
creating schedules for various kinds of scheduling
problems. However, manually designing DRs for all
possible scheduling conditions and scheduling criteria
is practically infeasible. For this reason, much of the
research has focused on the automatic design of DRs
using various methods, especially genetic programming.
However, even if genetic programming is used to design
new DRs to optimise a particular criterion, it will not
give good results for all possible problem instances to
which it can be applied. Due to the stochastic nature
of genetic programming, the evolution of DRs must be
performed several times to ensure that good DRs have
been obtained. However, in the end, usually only one
rule is selected from the set of evolved DRs and used
to solve new scheduling problems. In this paper, a DR
selection procedure is proposed to select the
appropriate DR from the set of evolved DRs based on the
features of the problem instances to be solved. The
proposed procedure is executed simultaneously with the
execution of the system, approximating the properties
of the problem instances and selecting the appropriate
DR for the current conditions. The obtained results
show that the proposed approach achieves better results
than those obtained when only a single DR is selected
and used for all problem instances.",
- }
Genetic Programming entries for
Marko Durasevic
Domagoj Jakobovic
Citations