How to cite this paper
Kabir, G & Sumi, R. (2012). Integrating fuzzy Delphi method with artificial neural network for demand forecasting of power engineering company.Management Science Letters , 2(5), 1491-1504.
Refrences
Abdel-Aal, R.E. (2008). Univariate modeling and forecasting of monthly energy demand time series using abductive and neural networks. Computers & Industrial Engineering, 54(4), 903-917.
Aburto, L., & Weber, R. (2007). Improved supply chain management based on hybrid demand forecasts. Applied Soft Computing, 7(1), 136-144.
Andrawis, R.R., Atiya, A.F., & El-Shishiny, H. (2011) Combination of long term and short term forecasts, with application to tourism demand forecasting. International Journal of Forecasting, 27, 870–886.
Bodyanskiy, T., & Popov, S. (2006). Neural Network approach to forecasting of quasiperiodic financial time series. European Journal of operation Research, 175(3), 1375-1366.
Br?nn?s, K., Hellstr?m, J., & Nordstr?m, J. (2002). A new approach to modeling and forecasting monthly guest nights in hotels. International Journal of Forecasting, 18(1), 19-30.
Carbonneau, R., Laframboise, K., & Vahidov, R. (2008). Application of machine learning techniques for supply chain demand forecasting. European Journal of Operational Research, 184(3), 1140-1154.
Chen, K.Y. (2011). Combining linear and nonlinear model in forecasting tourism demand. Expert Systems with Applications, 38(8), 10368-10376.
Chopra, S., & Meindl, P. (2010). Supply chain management: Strategy, planning and operation. 4th Ed., NJ: Prentice-Hall.
Choy, K. L., Lee, W. B., & Lo, V. (2003). Design of an intelligent supplier relationship management system: A hybrid case based neural network approach. Expert Systems with Applications, 24(2), 225–237.
Chu, F.L. (2009). Forecasting tourism demand with ARMA-based methods. Tourism Management, 30(5), 740-751.
Coshall, J.T., & Charlesworth, R. (2011). A management orientated approach to combination forecasting of tourism demand. Tourism Management, 32(4), 759-769.
Franses, P.H., & Dijk, D.V. (2005). The forecasting performance of various models for seasonality and nonlinearity for quarterly industrial production. International Journal of Forecasting, 21(1), 87-102.
Garcia-Ferrer, A., Juan, A.D., & Poncela, P. (2006). Forecasting traffic accidents using disaggregated data. International Journal of Forecasting, 22(2), 203-222.
Garetti, M., & Taisch, M. (1999). Neural networks in production planning and control. Production Planning & Control: The Management of Operations, 10(4), 324-339.
Gupta, R., Sachdeva, A., & Bhardwaj, A. (2010). Selection of 3pl Service Provider using Integrated Fuzzy Delphi and Fuzzy TOPSIS. Proceedings of the World Congress on Engineering and Computer Science, Vol II, October 20-22, 2010, San Francisco, USA.
Haykin, S. (2001). Neural Networks- A comprehensive Foundation’, 2nd edition, Pearson Education, Inc. Singapore.
Heij, C., Dijk, D.V., & Groenen, P.J.F. (2008). Macroeconomic forecasting with matched principal components. International Journal of Forecasting, 24(1), 87-100.
Herrera, M., Torgo, L., Izquierdo, J., & Garc?a, R.P. (2010). Predictive models for forecasting hourly urban water demand. Journal of Hydrology, 387(1-2), 141-150.
Ishikawa, A., Amagasa, M., Shiga, T., Tomizawa, G., Tatsuta, R., & Mieno, H. (1993). The max-min Delphi method and fuzzy Delphi method via fuzzy integration. Fuzzy Sets and Systems, 55(3), 241-253.
Law, R. (2000). Back-propagation learning in improving the accuracy of neural network-based tourism demand forecasting. Tourism Management, 21(4), 331-340.
Law, R., & Au, N. (1999). A neural network model to forecast Japanese demand for travel to Hong Kong. Tourism Management, 20(1), 89-97.
Lenard, M. J., Alam, P., & Madey, G.R. (1995). The application of neural networks and a qualitative response model to the auditor’s going concern uncertainty decision. Decision Sciences, 26(2), 209-227.
Li, S.T., Kuo, S.C., Cheng, Y.C., & Chen, C.C. (2010). Deterministic vector long-term forecasting for fuzzy time series. Fuzzy Sets and Systems, 161(13), 1852-1870.
Luxhoj, J.T., Riis, J.O., & Stensballe, B. (1996). A hybrid econometric-neural network modeling approach for sales forecasting. International Journal of Production Economics, 43(2-3), 175-192.
Manevitz, L., Bitar, A., & Givoli, D. (2005). Neural network time series forecasting of finite-element mesh adaptation. Neurocomputing, 63, 447-463.
Noorderhaben, N. (1995). Strategic decision making. UK: Addison-Wesley.
Palau, A., Velo, E., & Puigjaner, L. (1999). Use of Neural Networks and Expert Systems to Control A Gas/Solid Sorption Chilling Machine. International Journal of Refrigeration, 22(1), 59-66.
Pedregal, D.J., & Trapero, J.R. (2010). Mid-term hourly electricity forecasting based on a multi-rate approach. Energy Conversion and Management, 51(1), 105-111.
Petrovic, D., Xie, Y., & Burnham, K. (2006). Fuzzy Decision Support System for Demand Forecasting with a Learning Mechanism. Fuzzy Sets and Systems, 157(12), 1713-1725.
Piramuthu, S., Shaw, M., & Gentry, J. (1994). A classification approach using multilayered neural networks. Decision Support Systems, 11(5), 509–525.
Sayed, H.E., Gabbar, H.A., & Miyazaki, S. (2009). A Hybrid Statistical Genetic-based Demand Forecasting Expert System. Expert Systems with Applications, 36(9), 11662- 11670.
Skapura, D.M. (1996). Building Neural Networks. ACM Press, New York, 143-148.
Smith, B.L., Williams, B.M., & Oswald, R.K. (2002). Comparison of parametric and nonparametric models for traffic flow forecasting. Transportation Research Part C: Emerging Technologies, 10(4), 303-321.
Sozen, A., Arcaklioglu, E., & Ozkaymak, M. (2005) Turkey’s net energy consumption. Applied Energy, 81(2), 209-221.
Taylor, J.W. (2007). Forecasting daily supermarket sales using exponentially weighted quantile regression. European Journal of Operational Research, 178(1), 154-167.
Weatherford, L.R., & Kimes, S.E. (2003). A comparison of forecasting methods for hotel revenue Management. International Journal of Forecasting, 19(3), 401-415.
Winklhofer, H., & Diamantopoulos, A. (2003). A model of export sales forecasting behavior and performance: development and testing. International Journal of Forecasting, 19(2), 271-285.
Yelland, Y.M. (2010). Bayesian forecasting of parts demand. International Journal of Forecasting, 26(2), 374-396.
Aburto, L., & Weber, R. (2007). Improved supply chain management based on hybrid demand forecasts. Applied Soft Computing, 7(1), 136-144.
Andrawis, R.R., Atiya, A.F., & El-Shishiny, H. (2011) Combination of long term and short term forecasts, with application to tourism demand forecasting. International Journal of Forecasting, 27, 870–886.
Bodyanskiy, T., & Popov, S. (2006). Neural Network approach to forecasting of quasiperiodic financial time series. European Journal of operation Research, 175(3), 1375-1366.
Br?nn?s, K., Hellstr?m, J., & Nordstr?m, J. (2002). A new approach to modeling and forecasting monthly guest nights in hotels. International Journal of Forecasting, 18(1), 19-30.
Carbonneau, R., Laframboise, K., & Vahidov, R. (2008). Application of machine learning techniques for supply chain demand forecasting. European Journal of Operational Research, 184(3), 1140-1154.
Chen, K.Y. (2011). Combining linear and nonlinear model in forecasting tourism demand. Expert Systems with Applications, 38(8), 10368-10376.
Chopra, S., & Meindl, P. (2010). Supply chain management: Strategy, planning and operation. 4th Ed., NJ: Prentice-Hall.
Choy, K. L., Lee, W. B., & Lo, V. (2003). Design of an intelligent supplier relationship management system: A hybrid case based neural network approach. Expert Systems with Applications, 24(2), 225–237.
Chu, F.L. (2009). Forecasting tourism demand with ARMA-based methods. Tourism Management, 30(5), 740-751.
Coshall, J.T., & Charlesworth, R. (2011). A management orientated approach to combination forecasting of tourism demand. Tourism Management, 32(4), 759-769.
Franses, P.H., & Dijk, D.V. (2005). The forecasting performance of various models for seasonality and nonlinearity for quarterly industrial production. International Journal of Forecasting, 21(1), 87-102.
Garcia-Ferrer, A., Juan, A.D., & Poncela, P. (2006). Forecasting traffic accidents using disaggregated data. International Journal of Forecasting, 22(2), 203-222.
Garetti, M., & Taisch, M. (1999). Neural networks in production planning and control. Production Planning & Control: The Management of Operations, 10(4), 324-339.
Gupta, R., Sachdeva, A., & Bhardwaj, A. (2010). Selection of 3pl Service Provider using Integrated Fuzzy Delphi and Fuzzy TOPSIS. Proceedings of the World Congress on Engineering and Computer Science, Vol II, October 20-22, 2010, San Francisco, USA.
Haykin, S. (2001). Neural Networks- A comprehensive Foundation’, 2nd edition, Pearson Education, Inc. Singapore.
Heij, C., Dijk, D.V., & Groenen, P.J.F. (2008). Macroeconomic forecasting with matched principal components. International Journal of Forecasting, 24(1), 87-100.
Herrera, M., Torgo, L., Izquierdo, J., & Garc?a, R.P. (2010). Predictive models for forecasting hourly urban water demand. Journal of Hydrology, 387(1-2), 141-150.
Ishikawa, A., Amagasa, M., Shiga, T., Tomizawa, G., Tatsuta, R., & Mieno, H. (1993). The max-min Delphi method and fuzzy Delphi method via fuzzy integration. Fuzzy Sets and Systems, 55(3), 241-253.
Law, R. (2000). Back-propagation learning in improving the accuracy of neural network-based tourism demand forecasting. Tourism Management, 21(4), 331-340.
Law, R., & Au, N. (1999). A neural network model to forecast Japanese demand for travel to Hong Kong. Tourism Management, 20(1), 89-97.
Lenard, M. J., Alam, P., & Madey, G.R. (1995). The application of neural networks and a qualitative response model to the auditor’s going concern uncertainty decision. Decision Sciences, 26(2), 209-227.
Li, S.T., Kuo, S.C., Cheng, Y.C., & Chen, C.C. (2010). Deterministic vector long-term forecasting for fuzzy time series. Fuzzy Sets and Systems, 161(13), 1852-1870.
Luxhoj, J.T., Riis, J.O., & Stensballe, B. (1996). A hybrid econometric-neural network modeling approach for sales forecasting. International Journal of Production Economics, 43(2-3), 175-192.
Manevitz, L., Bitar, A., & Givoli, D. (2005). Neural network time series forecasting of finite-element mesh adaptation. Neurocomputing, 63, 447-463.
Noorderhaben, N. (1995). Strategic decision making. UK: Addison-Wesley.
Palau, A., Velo, E., & Puigjaner, L. (1999). Use of Neural Networks and Expert Systems to Control A Gas/Solid Sorption Chilling Machine. International Journal of Refrigeration, 22(1), 59-66.
Pedregal, D.J., & Trapero, J.R. (2010). Mid-term hourly electricity forecasting based on a multi-rate approach. Energy Conversion and Management, 51(1), 105-111.
Petrovic, D., Xie, Y., & Burnham, K. (2006). Fuzzy Decision Support System for Demand Forecasting with a Learning Mechanism. Fuzzy Sets and Systems, 157(12), 1713-1725.
Piramuthu, S., Shaw, M., & Gentry, J. (1994). A classification approach using multilayered neural networks. Decision Support Systems, 11(5), 509–525.
Sayed, H.E., Gabbar, H.A., & Miyazaki, S. (2009). A Hybrid Statistical Genetic-based Demand Forecasting Expert System. Expert Systems with Applications, 36(9), 11662- 11670.
Skapura, D.M. (1996). Building Neural Networks. ACM Press, New York, 143-148.
Smith, B.L., Williams, B.M., & Oswald, R.K. (2002). Comparison of parametric and nonparametric models for traffic flow forecasting. Transportation Research Part C: Emerging Technologies, 10(4), 303-321.
Sozen, A., Arcaklioglu, E., & Ozkaymak, M. (2005) Turkey’s net energy consumption. Applied Energy, 81(2), 209-221.
Taylor, J.W. (2007). Forecasting daily supermarket sales using exponentially weighted quantile regression. European Journal of Operational Research, 178(1), 154-167.
Weatherford, L.R., & Kimes, S.E. (2003). A comparison of forecasting methods for hotel revenue Management. International Journal of Forecasting, 19(3), 401-415.
Winklhofer, H., & Diamantopoulos, A. (2003). A model of export sales forecasting behavior and performance: development and testing. International Journal of Forecasting, 19(2), 271-285.
Yelland, Y.M. (2010). Bayesian forecasting of parts demand. International Journal of Forecasting, 26(2), 374-396.