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Advances in Artificial Neural Systems
Volume 2013 (2013), Article ID 284570, 17 pages
http://dx.doi.org/10.1155/2013/284570
Research Article

Variance Sensitivity Analysis of Parameters for Pruning of a Multilayer Perceptron: Application to a Sawmill Supply Chain Simulation Model

Centre de Recherche en Automatique de Nancy (CRAN-UMR 7039), Nancy-Université, CNRS, Campus Sciences, BP 70239, 54506 Vandoeuvre les Nancy Cedex, France

Received 6 May 2013; Revised 15 July 2013; Accepted 15 July 2013

Academic Editor: Chao-Ton Su

Copyright © 2013 Philippe Thomas et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Simulation is a useful tool for the evaluation of a Master Production/Distribution Schedule (MPS). The goal of this paper is to propose a new approach to designing a simulation model by reducing its complexity. According to the theory of constraints, a reduced model is built using bottlenecks and a neural network exclusively. This paper focuses on one step of the network model design: determining the structure of the network. This task may be performed by using the constructive or pruning approaches. The main contribution of this paper is twofold; it first proposes a new pruning algorithm based on an analysis of the variance of the sensitivity of all parameters of the network and then uses this algorithm to reduce the simulation model of a sawmill supply chain. In the first step, the proposed pruning algorithm is tested with two simulation examples and compared with three classical pruning algorithms from the literature. In the second step, these four algorithms are used to determine the optimal structure of the network used for the complexity-reduction design procedure of the simulation model of a sawmill supply chain.