Using genetic programming and simulation to learn how to dynamically adapt the number of cards in reactive pull systems
We show how to learn dynamically to adapt the number of cards in real time in token-based pull systems. We propose a Simulation-based Genetic Programming approach which does not need training sets. We illustrate how the approach can be implemented using Arena and ?GP. A reactive ConWIP example show the efficiency of the approach and of the knowledge extracted. The resulting decision tree can be used online by production managers or for self-adaptation issues.
genetic programming simulation learning reactive pull systems conwip kanban manufacturing systems self-adaptive systems