A neural network for the reduction of a Product Driven System emulation model

Philippe Thomas (1) , André Thomas (1) , Marie-Christine Suhner (1)

1 CRAN - Centre de Recherche en Automatique de Nancy

In new Intelligent Manufacturing Systems, Product Driven Systems (PDS) architectures require emulation tool (Thomas et al. 2008) to be developed. Discrete events simulation is often used to build such emulation tool, nevertheless this remains complex because of large scale problems. The goal of this paper is to propose a way to design a simulation model by reducing its complexity. According to theory of constraints, we build reduced models composed exclusively of bottlenecks and a neural network. In Particular, a multilayer perceptron is used. The structure of the network is determined by using a pruning procedure. This work highlights the impact of discrete data on the computational results. An application to a sawmill internal supply chain is suggested to validate the proposed approach.

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

Domaines: Autre; Automatique; Modélisation et simulation

Philippe Thomas, André Thomas, Marie-Christine Suhner. A neural network for the reduction of a Product Driven System emulation model. Production Planning and Control, 2011, 22 (8), pp.767-781. ⟨10.1080/09537287.2010.543560⟩. ⟨hal-00569837v2⟩