Multilayer perceptron for simulation models reduction: application to a sawmill workshop

Philippe Thomas (1) , André Thomas (1)

1 CRAN - Centre de Recherche en Automatique de Nancy

Simulation is often used to evaluate supply chain or workshop management. This simulation task needs models, which are difficult to construct. The aim of this work is to reduce the complexity of a simulation model design. The proposed approach combines discrete and continuous approaches in order to construct speeder and simpler reduced model. The simulation model focuses on bottlenecks with a discrete approach according to the theory of constraints. The remaining of the workshop must be taken into account in order to describe how the bottlenecks are fed. It is modeled through a continuous approach thanks to a neural network. In particular, we use a multilayer perceptron. The structure of the network is determined by using a pruning procedure. For validation, this approach is applied to the modelisation of a sawmill workshop.

Mots clés: multilayer perceptron; reduced model; simulation; neural network; supply chain; ANN

Domaines: Automatique; Modélisation et simulation

Philippe Thomas, André Thomas. Multilayer perceptron for simulation models reduction: application to a sawmill workshop. Engineering Applications of Artificial Intelligence, 2011, 24 (4), pp.646-657. ⟨10.1016/j.engappai.2011.01.004⟩. ⟨hal-00569936⟩