Using genetic programming and simulation to learn how to dynamically adapt the number of cards in reactive pull systems
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- @Article{Silva-Belisario:2015:ESA,
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author = "Lorena {Silva Belisario} and Henri Pierreval",
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title = "Using genetic programming and simulation to learn how
to dynamically adapt the number of cards in reactive
pull systems",
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journal = "Expert Systems with Applications",
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volume = "42",
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number = "6",
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pages = "3129--3141",
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year = "2015",
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month = "15 " # apr,
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keywords = "genetic algorithms, genetic programming, Kanban,
ConWIP, Manufacturing systems, Reactive pull systems,
Self-adaptive systems, Learning, Simulation",
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ISSN = "0957-4174",
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DOI = "doi:10.1016/j.eswa.2014.11.052",
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URL = "http://www.sciencedirect.com/science/article/pii/S0957417414007519",
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size = "13 pages",
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abstract = "Pull control systems are now widely used in many types
of production systems. For those based on cards,
determining their number is an important issue. When
the system is submitted to changes in supply and
demand, several researchers have demonstrated the
benefits of changing this number dynamically. Defining
when and how to do so is known as a difficult problem,
especially when such modifications in customer demands
are unpredictable and the system behaviour is
stochastic. This paper proposes a Simulation-based
Genetic Programming approach to learn how to decide,
i.e., to generate a decision logic that specifies under
which circumstances it is worth modifying the number of
cards. It aims at eliciting the underlying knowledge
through a decision tree that uses the current system
state as input and returns the suggested modifications
of the number of cards as output. Contrarily to the few
learning approaches presented in the literature, no
training set is used, which represents a major
advantage when real-time decisions have to be learnt.
An adaptive ConWIP system, taken from the literature,
is used to illustrate the relevance of our approach.
The comparison made shows that it can yield better
results, and generate the knowledge in an autonomous
way. This knowledge is expressed under the form of a
decision tree that can be understood and exploited by
the decision maker, or by an automated on-line decision
support system providing a self-adaptation component to
the manufacturing system.",
- }
Genetic Programming entries for
Lorena Silva-Belisario
Henri Pierreval
Citations