Contribution de l'apprentissage par simulation a l'auto-adaptation des systemes de production

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Lorena {Silva Belisario}",
  title =        "Contribution de l'apprentissage par simulation a
                 l'auto-adaptation des systemes de production",
  title_anglais = "Simulation-based machine learning for the
                 self-adaptation of manufacturing systems",
  school =       "Universite Blaise Pascal",
  year =         "2015",
  address =      "Clermont-Ferrand 2, France",
  month =        "12 " # nov,
  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",
  bibsource =    "OAI-PMH server at",
  contributor =  "de Mod{\'e}lisation et d'optimisation des Syst{\`e}mes
                 Laboratoire d'Informatique and Henri Pierreval",
  identifier =   "NNT : 2015CLF22617; tel-01325229",
  language =     "fr",
  oai =          "oai:HAL:tel-01325229v1",
  source =       "Autre. Universit{\'e} Blaise Pascal - Clermont-Ferrand
                 II, 2015. Fran{\c c}ais. ",
  URL =          "",
  URL =          "",
  URL =          "",
  URL =          "",
  URL =          "",
  size =         "260 pages",
  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
  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
  notes =        "In French. Expected online June 2016.

                 Supervisor: Henri Pierreval

                 National Thesis number : 2015CLF22617


Genetic Programming entries for Lorena Silva-Belisario