Using genetic programming and simulation to learn how to dynamically adapt the number of cards in reactive pull systems

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  author =       "Lorena {Silva Belisario} and Henri Pierreval",
  title =        "Using genetic programming and simulation to learn how
                 to dynamically adapt the number of cards in reactive
                 pull systems",
  journal =      "Expert Systems with Applications",
  volume =       "42",
  number =       "6",
  pages =        "3129--3141",
  year =         "2015",
  month =        "15 " # apr,
  keywords =     "genetic algorithms, genetic programming, Kanban,
                 ConWIP, Manufacturing systems, Reactive pull systems,
                 Self-adaptive systems, Learning, Simulation",
  ISSN =         "0957-4174",
  DOI =          "doi:10.1016/j.eswa.2014.11.052",
  URL =          "",
  size =         "13 pages",
  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