M. Chiu and G. Lin, Collaborative supply chain planning using the artificial neural network approach, Journal of Manufacturing Technology Management, vol.15, issue.8, pp.787-796, 2004.
DOI : 10.1108/17410380410565375

L. Chwif, R. J. Paul, M. R. Pereira, and . Barretto, « Discret event simulation model reduction: A causal approach », Simulation Modelling Practice and Theory, pp.930-944, 2006.

T. Cibas, F. Fogelman-soulié, P. Gallinari, and S. Raudys, Variable selection with neural networks, Variable selection with neural networks », pp.223-248, 1996.
DOI : 10.1016/0925-2312(95)00121-2
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.34.357

«. Muller, Neural modelling for time series: a statistical stepwise method for weight elimination, IEEE Trans. on Neural Networks, vol.6, issue.6, pp.1355-1264, 1995.

G. Cybenko and «. , Approximation by superposition of a sigmoïdal function, Math. Control Systems Signals, pp.303-314, 1989.

P. Demartines, Analyse de données par réseaux de neurones auto-organisés, thèse de l'institut national polytechnique de Grenoble, 1995.

H. Drucker, « Effect of pruning and early stopping on performance of a boosting ensemble », Computationnal Statistics and Data Analysis, pp.393-406, 2002.

A. P. Engelbrecht, A new pruning heuristic based on variance analysis of sensitivity information, IEEE Transactions on Neural Networks, vol.12, issue.6, pp.1386-1399, 2001.
DOI : 10.1109/72.963775

K. Funahashi, On the approximate realization of continuous mappings by neural networks, Neural Networks, vol.2, issue.3, pp.183-192, 1989.
DOI : 10.1016/0893-6080(89)90003-8

S. Gadat and L. Younes, « A stochastic algorithm for feature selection in pattern recognition, J. of Machine Learning Research, vol.8, pp.509-547, 2007.

E. Goldratt and J. Cox, The Goal : A process of ongoing improvement, pp.978-0884270614, 1992.

B. Hassibi and D. G. , Stork, « Second order derivatives for network pruning: optimal brain surgeon » Advances in Neural Information Processing Systems, pp.164-171, 1993.

G. S. Innis and E. Rexstad, Simulation model simplification techniques, SIMULATION, vol.41, issue.1, pp.41-48, 1983.
DOI : 10.1177/003754978304100101

P. Leray and P. Gallinari, FEATURE SELECTION WITH NEURAL NETWORKS, Behaviormetrika, vol.26, issue.1, pp.145-166, 1999.
DOI : 10.2333/bhmk.26.145
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.54.4570

X. Liang, Removal of hidden neurons in multilayer perceptrons by orthogonal projection and weight crosswise propagation, Neural Computing and Applications, vol.56, issue.2, pp.57-68, 2007.
DOI : 10.1007/s00521-006-0057-7

P. Lopez and F. Roubellat, Ordonnancement de la production, pp.2-7462, 2001.

L. Ma and K. Khorasani, New training strategies for constructive neural networks with application to regression problems », Neural Network Le Management par les contraintes en gestion industrielle. Trouver le bon déséquilibre, Editions d'Organisation, pp.589-609, 1994.

K. J. Musselman, « Guideline for simulation project success, Proc. of the 1993 Winter Simulation Conf, pp.58-64, 1993.

M. Norgaard, System identification and control with neural networks, PhD, Institute of Automation, Technical university of Denmark, 1996.

E. H. Page, D. M. Nicol, O. Balci, R. M. Fujimoto, P. A. Fishwick et al., « An aggregate production planning framework for the evaluation of volume flexibility, Proc. of the 1999 Winter Simulation Conference, pp.1509-1520, 1999.

M. Pidd, Five simple principle of modelling, Proceedings of the 28th conference on Winter simulation , WSC '96, pp.721-728, 1996.
DOI : 10.1145/256562.256794

I. Rivals and L. Personnaz, Neural-network construction and selection in nonlinear modeling, IEEE Transactions on Neural Networks, vol.14, issue.4, pp.804-819, 2003.
DOI : 10.1109/TNN.2003.811356
URL : https://hal.archives-ouvertes.fr/hal-00797670

S. Shervais, T. T. Shannon, and G. G. , Intelligent supply chain management using adaptive critic learning, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, vol.33, issue.2, pp.235-244, 2003.
DOI : 10.1109/TSMCA.2003.809214
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.642.3140

R. Setiono, Feedforward Neural Network Construction Using Cross Validation, Neural Computation, vol.13, issue.12, pp.2865-2877, 2001.
DOI : 10.1109/72.728361

A. Thomas and P. Charpentier, « De la pertinence de modèles réduits pour la prise de decision en réordonnancement, Proc. of the 2 nd International Conf. on Integrated Design and Production CPI'01, 2001.

A. Thomas and P. Charpentier, Reducing simulation models for scheduling manufacturing facilities, European Journal of Operational Research, vol.161, issue.1, pp.111-125, 2005.
DOI : 10.1016/j.ejor.2003.08.042

P. Thomas and G. Bloch, Initialization of one hidden layer feed-forward neural networks for non-linear system identification, Proc. of the 15 th IMACS World Congress on Scientific Computation Modelling and Applied Mathematics WC'97, pp.295-300, 1997.

P. Thomas, G. Bloch, F. Sirou, and V. Eustache, « Neural modeling of an induction furnace using robust learning criteria, J. of Integrated Computer Aided Engineering, vol.6, issue.1, pp.5-23, 1999.

P. Thomas and A. Thomas, Expérimentation de la réduction d'un modèle de simulation par réseau de neurones : cas d'une scierie », MOSIM'08, pp.31-33, 2008.

P. Thomas, D. Choffel, and A. Thomas, « Simulation reduction models approach using neural network » EUROSIM'08, 1-3 april, 2008.

P. Thomas and A. Thomas, « Elagage d'un perceptron multicouches : utilisation de l'analyse de la variance de la CIFA, pp.3-5, 2008.

S. C. Ward, Arguments for Constructively Simple Models, Journal of the Operational Research Society, vol.40, issue.2, pp.141-153, 1989.
DOI : 10.1057/jors.1989.19

J. Xu and D. W. Ho, A new training and pruning algorithm based on node dependence and Jacobian rank deficiency, Neurocomputing, vol.70, issue.1-3, pp.544-558, 2006.
DOI : 10.1016/j.neucom.2005.11.005

B. P. Zeigler, Theory of Modelling and Simulation, IEEE Transactions on Systems, Man, and Cybernetics, vol.9, issue.1, 1976.
DOI : 10.1109/TSMC.1979.4310082

X. Zeng and D. S. Yeung, Hidden neuron pruning of multilayer perceptrons using a quantified sensitivity measure, Neurocomputing, vol.69, issue.7-9, pp.825-837, 2006.
DOI : 10.1016/j.neucom.2005.04.010