Bibliographie. Antony RN, 1988, The management control function, HBS Press, Boston

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1 Bibliographie Antony RN, 1988, The management control function, HBS Press, Boston Aslog, 1999, ERP/SCM: bilan et perspectives; Etude Aslog Progilog Baglin G, Bruel O, 2005, Management industriel et logistique;conception et pilotage de la Supply Chain, Economica Banks D, 1997, Radical artificial intelligence, a postmodern approach, Dpt of Computer Science, University of Arkansas, Fayetteville Bentayeb F, 2003, Systemes d information décisionnels analyse multidimentionnelle, Université Lyon 2 Bentayeb F, 2003, Systemes d information décisionnels ECD et administration des données, Université Lyon 2 Bentayeb F, 2003, Systemes d information décisionnels Entreposage de données classiques, Université Lyon 2 Bentayeb F, 2003, Systemes d information décisionnels Introduction au processus décisionnel, Université Lyon 2 Bentayeb F, 2003, Systemes d information décisionnels Structures de données multiformes, Université Lyon 2 Bergeret F, Besse P, Simulated annealing, weighted simulated annealing and genetic algorithm at work, Laboratoire de Statistique et Probabilités, U.M.R. CNRS 5583, Université Paul Sabatier Toulouse Berkstresser G, Little TJ, 1999, Integrated Supply Chain Analysis and Decision Support, National Textile Annual Report Betzinger E, 2001, tutoriel internet Betzinger E, 2006, E-logistique & E-supplychain, IECS, URS Strasbourg Bishop C, 1995, Neural Networks for pattern recognition, Oxford, Clarendon Press Blattberg R, Hoch S, 1991, Modèles à base de données et intuition managériale : 50% modèle + 50% manager, RAM, vol VI, 4/91 Bollerslev T, Chou R, 1992, Arch modeling in finance; a review of the theory and empirical evidence, JE 52 p5-59

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3 Crone, S, 2005 Stepwise selection of Artificial neural netwoks models for time series prediction, Dpt of Management Science Lancaster University Management School Crone, S, 2005, Prediction of whire noise time series using artificial neural networks and asymetric cost functions, Institute of Information Systems, University of Hamburg Crone, S, Kausch H, Pressmar D, 2006, Prediction of the CATS benchmark using a business forecasting approach to multilayer perceptron modelling Institute of Information Systems, University of Hamburg Crone S, 2005, Prediction of white noise time series using artificial neural networks and asymetric cost functions, Institute of Information Systems, University of Hamburg Crone S., Lessmann S., Stahlbock R., (), Utility based Data Mining for time series analysis cost sensitive learning for neural predictors, University of Hamburg Dent J, 1990, Strategy, accounting and control : some posibilities for accounting research, Accounting organization and society, vol 15, pp3-25 Duch w, 1996, Computational intelligence methods and data Duch W, Jankowski N, 1997, New neural transfer functions Department of Computer Methods, Nicholas Copernicus University, ul. Grudzi adzka 5, Toru n Poland Efron B, Tibshirani R, 1986, Boostrap methods for standard errors, confidence intervals and other measures of statistical accuracy, Statistical Science 1, pp Efron B 1979, Bootstrap methods : another look at the Jacknife, Annals of Statistics 7, pp 1-26 Elman JL, 1991, Distributed representations, simple recurrent networks, and grammatical structure. Machine Learning 7, Evrard Y, Pras B, Roux E, 1997,Market; études et recherches en marketing; fondements et méthodes, Nathan, Collection Connaître et pratiquer la gestion Faraway J, 1995, Time series forecasting using neural networks; a case study, Research report of the Statistics Group, University of Bath Fogelman F, 1997, Réseaux de neurones et statistiques. Une introduction in Thiria Lechevallier, Statistiques et methodes neuronales, Dunod Forbes C, Snyder R, 2000, Bayesian Exponential Smoothing, Monash University Forrester JW, 1958, Industrial dynamics - a major breathrough for decision makers, in Harvard Business Review, vol 36, n 4, pp37-66 Gaspard F, 2001, Localisation et calibration avec un zoom, Thèse de Sciences pour lingénieur, Sophia Antipolis.

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5 Harrington P, 1993, Sigmoid Transfer Functions in Backpropagation Neural Networks, Anal. Chem. 65, pp Hebb, DO, 1949, The organization of behaviour, a neurophysiological theory, Wiley New York Holweg M, 2001, Systems methodology in manufacturing and logistics research a critical review Hussenot A, 2006, Appropriation collective dune solution TIC de gestion, un processus social itératif, in Grimand A, 2006, L appropriation des outils de gestion ; vers de nouvelles perspectives théoriques? Publications de luniversité de Saint-Etienne Iovlev S, 2001, Calcul exact des dérivées d un réseau de neurones multi-couches à propagation avant, Samos, Paris Holland JH, Adaptation in Natural and Artificial Systems, The Mit Press Justin J, 2006, Analyse dune coproduction doutils de contrôle de gestion dans le cadre dune recherche action, in Grimand A, 2006, L appropriation des outils de gestion ; vers de nouvelles perspectives théoriques? Publications de luniversité de Saint-Etienne Kaashoeck J, van Dijk, H, 1998, Neural networks analysis of varying exchange rates, Unviersity of Rotterdam Kallel R, Cottrell M, Vigneron V, Bootstrap for neural model selection, European symposium on artificial neural networks, Burges, April 2000, ISBN B 00-5, pp Koehler A, 1999, Forecasting Models and Prediction Intervals for the Multiplicative Holt- Winters Method, Ddpt of Econometrics and business Statistics, Monash University Koehler A, Snyder R, 1999, Forecasting Models and Prediction Intervals for the Multiplicative Holt-Winters Method, Department of econometrics and Business Statistics, Monash University Kohonen T, Self organisation of topologically correct feature maps, Biological cybernetics 43, pp59-69 Kohonen T, 1988 Self organisation and associative memory, Springer series in information science 8, Springer Verlag,, Berlin Kohonen T, 1994, Self organising maps, Springer Verlag Kohonen T, Kangas J, SOMPAK, the self organising map program package, Helsinki, University of Technology, Laboratory of computer and information science, Finland Lebart L, Morineau A, Piron M, 1995, Statistique exploratoire multidimensionnelle, Dunod

6 Leray P, 2003, Series temporelles et réseaux de neurones, insa, dpt ASI Laboratoire PSI Lessmann S, Crone S, 2005, Utility based datamining for time series analysis; cost sensitive learning for neural predictors, University of Hamburg, Institute of Information Systems Lewkowicz J, Epistémologie et connaissance en Sciences de Gestion; débats et issues envisageables, URS Strasbourg Makridakis SG, Wheelwright SC, Hyndman RJ, 1998, Forecasting, methods and applications, John Wiley & Sons, New York Mangeas M, 1993, Propriétés Statistiques des Modèles Paramétriques Non-linéaires de Prévision de Séries Temporelles Application aux Réseaux de Neurones a propagation directe, These, Universite Paris, Ufr Mathematiques et Informatique Marchal A, 2006, Logistique globale ( Supplychain Management), Ellipses Marchal A, 2000, Gestion des stocks, Universite de Nancy 2, Ingenilog McCulloch W, Pitts W, 1943, A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics, 7: Michigan Press, 1975 Pascal A, Thomas C, Appropriation des TIC : vers une méthodologie de co-conception orientée usage. Le cas KMP,, in Grimand A, 2006, L appropriation des outils de gestion ; vers de nouvelles perspectives théoriques? Publications de luniversité de Saint-Etienne Pimor Y, 1998, Logistiques; techniques et mises en œuvre, Dunod, Collection Genie industriel Py B, 1990, Statistiques descriptives, Economica Rajopadhye M, Ben Ghalia M, Wang P, Forecasting uncertain hotel room demand, Department of Electrical and Computer Engineering, Duke University Ripley BD, Pattern recognition via neural networks, Oxford, To appear in the proceedings of Interface th Symposium on the Interface: Computing Science and Statistics Rosenblatt F, 1962, Principles of neurodynamics, Spartan, New York Rosin P, Fierens F, Improving neural networks generalization, Institute for Remote Sensing Applications, Joint Research Center, Ispra Rzempoluck E, 1998, Neural network data analysis using simulnet, Springer Verlag Saporta G, 1990 Probabilités, analyse des données et statistique, Technip Siarry P, Dréo J, Pétrowski A, Taillard E, 2003, Métaheuristiques pour l optimisation difficile, Eyrolles

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