Contribution de l'apprentissage par simulation a l'auto-adaptation des systemes de production
Created by W.Langdon from
gp-bibliography.bib Revision:1.7970
- @PhdThesis{Silva-Belisario:thesis,
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author = "Lorena {Silva Belisario}",
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title = "Contribution de l'apprentissage par simulation a
l'auto-adaptation des systemes de production",
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title_anglais = "Simulation-based machine learning for the
self-adaptation of manufacturing systems",
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school = "Universite Blaise Pascal",
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year = "2015",
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address = "Clermont-Ferrand 2, France",
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month = "12 " # nov,
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keywords = "genetic algorithms, genetic programming, microGP,
linear genetic programming, Manufacturing systems,
self-adaptation, decision support, knowledge
extraction, machine learning, simulation, Fabrication,
Systemes flexibles de -- Theses et ecrits academiques
Programmation genetique (informatique) -- Theses et
ecrits academiques Systemes de production
Auto-adaptation Aide a la decision Extraction de
connaissances Apprentissage automatique Simulation
Programmation genetique lineaire",
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bibsource = "OAI-PMH server at api.archives-ouvertes.fr",
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contributor = "de Mod{\'e}lisation et d'optimisation des Syst{\`e}mes
Laboratoire d'Informatique and Henri Pierreval",
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identifier = "NNT : 2015CLF22617; tel-01325229",
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language = "fr",
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oai = "oai:HAL:tel-01325229v1",
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source = "Autre. Universit{\'e} Blaise Pascal - Clermont-Ferrand
II, 2015. Fran{\c c}ais. ",
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URL = "http://www.theses.fr/2015CLF22617",
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URL = "http://www.sudoc.fr/191999954",
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URL = "https://tel.archives-ouvertes.fr/tel-01325229",
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URL = "https://tel.archives-ouvertes.fr/tel-01325229/document",
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URL = "https://tel.archives-ouvertes.fr/tel-01325229/file/SILVA_BELISARIO_2015CLF22617.pdf",
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size = "260 pages",
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abstract = "Manufacturing systems must be able to continuously
adapt their characteristics to cope with the different
changes that occur along their life, in order to remain
efficient and competitive. These changes can take the
form of the evolution of customers demand for instance.
It is essential for these systems to determine when and
how to adapt (e.g., through changes in capacities).
Unfortunately, such issues are known to be difficult.
As manufacturing systems are complex, dynamic and
specific in nature, their managers do not always have
all the necessary expertise nor accurate enough
forecasts on the evolution of their system. This thesis
aims at studying the possible contributions of machine
learning to the self-adaptation of manufacturing
systems. We first study how the literature deals with
self-adaptation and we propose a conceptual framework
to facilitate the analysis and the formalization of the
associated problems. Then, we study a learning strategy
relying on models, which presents the advantage of not
requiring any training set. We focus more precisely on
a new approach based on linear genetic programming that
iteratively extracts knowledge from a simulation model.
Our approach is implemented using Arena and microGP. We
show its benefits by applying it to increase/decrease
the number of cards in a pull control system, to move
machines or to change the inventory replenishment
policy. The extracted knowledge is found to be relevant
for continuously determining how each system can adapt
to evolutions. It can therefore contribute to provide
these systems with some intelligent capabilities.
Moreover, this knowledge is expressed in the simple and
understandable form of a decision tree, so that it can
also be easily communicated to production managers in
view of their everyday use. Our results thus show the
interest of our approach while opening many research
directions.",
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abstract = "Pour rester performants et competitifs, les systemes
de production doivent etre capables de s'adapter pour
faire face aux changements tels que l'evolution de la
demande des clients. Il leur est essentiel de pouvoir
determiner quand et comment s'adapter (capacites,
etc.). Malheureusement, de tels problemes sont connus
pour etre difficiles. Les systemes de production etant
complexes, dynamiques et specifiques, leurs
gestionnaires n'ont pas toujours l'expertise necessaire
ni les previsions suffisantes concernant l'evolution de
leur systeme. Cette these vise a etudier la
contribution que peut apporter l'apprentissage
automatique a l'auto-adaptation des systemes de
production. Dans un premier temps, nous etudions la
facon dont la litterature aborde ce domaine et en
proposons un cadre conceptuel dans le but de faciliter
l'analyse et la formalisation des problemes associes.
Ensuite, nous etudions une strategie d'apprentissage a
partir de modeles qui ne necessite pas d'ensemble
d'apprentissage. Nous nous interessons plus precisement
a une nouvelle approche basee sur la programmation
genetique lineaire visant a extraire des connaissances
iterativement a partir d'un modele de simulation pour
determiner quand et quoi faire evoluer. Notre approche
est implementee a l'aide d'Arena et uGP. Nous
l'appliquons a differents exemples qui concernent
l'ajout/retrait de cartes dans un systeme a flux tire,
le demenagement de machines ou encore le changement de
politique de reapprovisionnement. Les connaissances qui
en sont extraites s'averent pertinentes et permettent
de determiner en continu comment chaque systeme peut
s'adapter a des evolutions. De ce fait, elles peuvent
contribuer a doter un systeme d'une forme
d'intelligence. Exprimees sous forme d'un arbre de
decision, elles sont par ailleurs facilement
communicables a un gestionnaire de production. Les
resultats obtenus montrent ainsi l'interet de notre
approche tout en ouvrant de nombreuses voies de
recherche.",
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notes = "In French. Expected online June 2016.
Supervisor: Henri Pierreval
National Thesis number : 2015CLF22617
oai:HAL:tel-01325229v1
",
- }
Genetic Programming entries for
Lorena Silva-Belisario
Citations