___

• Christopher M., Logistic and Supply Chain Management, Pitman Publishing, London, UK, 1994, pp.30.
• Tozan H., Vayvay O., Analyzing Demand Variability Through SC Using Fuzzy Regression and Grey GM(1,1) Forecasting Models, Information Sciences 2007, World Scientific, 2007, pp. 1088--1094.
• Forrester J., Industrial Dynamics: A major breakthrough for decision makers, Harvard Business Review, 36, 1958, pp. 37--66.
• Forrester J., Industrial Dynamics, MIT Press, Cambridge, MA, 1961.
• Sterman J.D., Modeling Managerial Behavior: Misperception of Feedback in a Dynamic Decision Making Experiment, Management Science, 35, 1989, pp.321-399.
• Sterman J.D., Deterministic Chaos in an Experimental Economic System, Journal of Economic Behavior and Organization, 12, 1989, pp.1-28.
• http://beergame.uni-klu.ac.at/bg.htm, 2008.
• Larsen E.R., Morecroft J.D., Thomsen J.S., Complex Behavior in a Production- Distribution model, European Journal of Operation Research, 119, 1999, pp. 61-14.
• Lee H. L., Padmanabhan V., Whang S., The Bullwhip Effect in Supply Chains, MIT Sloan Management Rev., 38, 1997a, pp. 93-10 2.
• Lee H. L., Padmanabhan V., Whang S., Information Distortion in a Supply Chain: The bullwhip effect, Management Science, 43, 1997b, pp. 546--558.
• Lee H. L., Aligning Supply Chain Strategies with Product Uncertainties, California Management Review, 44, 2002, pp. 105-119.
• Lee H. L., Padmanabhan V., Whang S., Information Distortion in a Supply Chain: The bullwhip effect, Management Science, 50, 2004, pp. 1875--1886.
• Baganha M., Cohen M., The Stabilizing Effect of Inventory in Supply Chains, Operations Research, 46, 1998, pp.572-583.
• Graves S. C., A Single Inventory Model foe a Nonstationary Demand Process, Manufacturing &Service Operations Management, 1, 1999, pp.50-61.
• Drezner Z., Ryan J., Simchi-Levi D., Quantifiying the Bullwhip Effect: The impact of forecasting, lead time and information, Management Science, 46 (3), 2000, pp.436-443.
• Chen F., Drezner Z., Ryan J., Simchi-Levi D.,The Impact of Exponential Smoothing Forecasts on the Bullwhip Effect, Naval Research Logistics, 47, 2000, pp. 269-286.
• Li G., Wang S., Yan H., Yu G., Information Transportation in Supply Chain, Computers and Operation Research, 32, 2005, pp. 707-725.
• Dejonckheere J., Disney S.M., Lambrecht M., Towill D.R., Tranfer function analysis of forecasting included bullwhip in supply chains, Int.J. Production Economics, 78, 2002, pp. 133-144.
• Dejonckheere J., Disney S.M., Lambrecht M., Towill D.R., Measuring and avoiding the bullwhip effect: A control theoretic approach, European Journal of Operational Research, 147 2003, pp.567-590.
• Dejonckheere J., Disney S.M., Lambrecht M., Towill D.R., The impact of information enrichment on the bullwhip effect in supply chains: A control engineering perspective, European Journal of Operational Research, 153, 2004, pp. 727-750.
• Disney S.M., Towill D.R., On the bullwhip and inventory variance produced by an ordering policiy, International Journal of Management Science, 31 2003a, pp. 157- 167.
• Disney S.M., Towill D.R., The effect of vendor managed inventory (VMI) Dynamics on the bullwhip effect in supply chains, International Journal of Production Economics, 85 (2003b) 199-215.
• Disney S.M., Towill D.R., Velde W., Variance amplification and the golden ratio in production and inventory control, International Journal of Production Economics, 85, 2004, pp. 295-309
• Disney S.M., Farasyn I., Lambrecht M., Towill D.R., Van de Velde W. Taming the bullwhip effect whilst watching customer service in a singular supply chain echelon, European Journal of Operational Research, 173, 2006, pp.151-172.
• Gaery S., Disney S.M., Towill D.R., On bullwhip in supply chains-historical review, present practice and expected future impact, International Journal of Production Echonomics, 101, 2006, pp. 2-18.
• Song Q., Chissom B. S., Forecasting Enrollments with Fuzzy Time Series, Fuzzy Sets and Systems, 54, 1993, pp. 1-9.
• Song Q., Chissom B. S., Fuzzy Time Series and Its Models, Fuzzy Sets and Systems, 54, 1993, pp. 269-277.
• Song Q., Chissom B. S., Forecasting Enrollments with Fuzzy Time Series Part II, Fuzzy Sets and Systems, 62, 1994, pp. 1-8.
• Wang C.H., Predicting Tourism Demand Using Fuzzy Time Series and Hybrid Grey Theory, Tourism Management, 25, 2004, pp.367-374.
• Li S., Cheng Y., A Hidden Markov Model-based Forecasting Model for Fuzzy Time Series, WSEAS Transactions on System, 5, 2006, pp. 1919-1925
• Kahraman C., Fuzzy Applications in Industrial Engineering,, 1th edt., Springer, 2006, pp.40–55.
• Tanaka H., Uejima S., Asai K., Fuzzy Linear Regression Model, IEEE Trans. System, Man and Cybernet, 12 , 1982, pp. 903-907.,
• Tanaka H., Watada J., Possibilistic Linear Systems and Their Application to the Linear Regression Model, Fuzzy Sets and Systems, 27, 1988, pp.275–289.
• Wang, H.F., Tsaur R.H., Insight of a Fuzzy Regression Model, Fuzzy Sets and Systems, 2000, 112, pp. 355-369.
• Escoda I., Ortega A., Sanz A., Herms A., Demand Forecasting by Neuro-Fuzzy Techniques, Proceedings of Sixth IEEE Int.Conf. on Fuzzy Systems, 1982, pp. 1381- 1386.
• Kuo R.J., A Sales Forecasting System Based on Fuzzy Neural Network with Inıtial Weights Generated by Genetic Algorithm, European Journal of Operational Research, 129, 2001, pp.496-517.
• George A., Ucenic C., Forecasting the Wind Energy Production Using a Neuro-fuzzy Model, WSEAS Transactions on Environment and Development, 2, 2006, pp. 823-829
• Tozan H., Vayvay O., Effects of Fuzzy Forecasting Models on Supply Chain Performance, Advanced Topics on Fuzzy Systems, WSEAS, 2008, pp.107-113.
• Ross T. J., Fuzzy Logic with Engineering Applications, 2th edt., John Wiley and Sons, 2005, pp.555-567.
• Hwang J., Chen S.M., Lee C.H., Handling Forecasting Problems Using Fuzzy Time Series, Fuzzy Sts and Systems, 100, 1998, pp.217-228.
• Hecht-Nielsen R., Neurocomputing, Addision-Wesley, 1990.
• Maduko A., Developing and Testing a Neuro-Fuzzy Classification System for IOS Data in Asthmatic Children, Texas University Press, 2007.
• McCullough B., Pitts W., A Logical calculus of the Ideas Immanent in Nervous Activity, Bulletin on Mathematical Biophysics, 5, 1973, pp.115-133.
• Rosenblatt F., The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain, Psychological Review, 65, 1958, pp.386-408.
• Parker D.B., Learning Logic, Invention Report S81-64, Stanford University Press, 1982.
• Rymelhart D., McClelland J., Paralel Distribution Processing: Explorations in the Microstructure of Cognition, MIT Press, 1, 1986
• Jang J.S., ANFIS: Adaptive Network Based Fuzzy Interference System, IEEE Transactions on Systems, Man and Cybernetics, 23, 1993, pp.665-684.
• Paik S.K., Analysis of the Causes of Bullwhip Effect in Supply Chain: A Simulation Approach, George Washington University Press, 2003