اجلی، مهدی؛ صفری، حسین (۱۳۹۰). ارزیابی عملکرد واحدهای تصمیمگیری با استفاده از مدل ترکیبی شبکههای عصبی پیشبینیکننده عملکرد و تحلیل پوششی دادهها (مورد مطالعه: شرکت ملی گاز ایران). نشریه مهندسی صنایع، ۴۵(۱)، 13-29. حجازی، رضوان؛ انواری رستمی، علی اصغر؛ مقدسی، مینا (۱۳۸۷). تحلیل بهرهوری کل بانک توسعه صادرات ایران و رشد بهرهوری شعب آن با استفاده از تحلیل پوششی دادهها (DEA). فصلنامه مدیریت صنعتی، ۱(۱)، 39-50. علی نژاد، علیرضا (۱۳۹۷). ارائه یک روش ترکیبی از مدل سروکوال و تحلیل پوششی داده در رتبهبندی کیفیت خدمات. فصلنامه مطالعات مدیریت صنعتی، ۱۶(۴۸)، 153- ۱۸۱. علیرضائی، محمدرضا؛ افشاریان، محسن؛ تسلیمی، وحید (۱۳۸۶). ارائه راهکارهای منطقی بهبود عملکرد شعب بانکها به کمک مدلهای تعمیمیافته تحلیل پوششی دادهها. پژوهشنامه اقتصادی، ۷(۴)، 263- ۲۸۳. کاظمی، مصطفی؛ منظم ابراهیمپور، شیلا؛ ایل بیگی، علیرضا (۱۳۹۲). بررسی کارایی نواحی مختلف شهرداری مشهد با رویکرد تحلیل پوششی دادهها. فصلنامه برنامهریزی شهری، ۴(۱۵)، 113- ۱۳۲. هیلیر، فردریک؛ لیبرمن، جرالد (۱۳۹۱). پیشبینی و مدیریت موجودیها (ترجمه محمدعلی فائضی راد و عطیه حقیقت). تهران: نشر ترمه. References Adhikari, R. (2015). A neural network based linear ensemble framework for time series forecasting. Neurocomputing, 157, 231-242. Ajalli, M. & Safari, H. (2011). Analysis of the Technical Efficiency of the Decision Making Units Making Use of the Synthetic Model of Performance Predictor Neural Networks, and Data Envelopment Analysis (Case Study: Gas National Co. of Iran). Journal of Industrial Engineering, 45(1), 13-29. (in Persian) Alinezhad, A. (2018). A combined method of data envelopment analysis and SERVQUAL model in ranking of service quality. Industrial Management Studies, 16(48), 153-181. (in Persian) Alirezaee, M. R., Afsharian, M. & Taslimi, V. (2008). Provide Rational Solutions for Improving Bank's Branch Performance by Generalized Models of DEA. Economics Research, 7(4), 263-283. (in Persian) Ashrafi, A., Seow, H., Lee, L.S., & and Lee, C.G. (2013). The efficiency of the hotel industry in Singapore Tourism Management, 37, 31-4. Athanassopoulos, A. D. & Curram, S. (1996). A comparison of data envelopment analysis and artificial neural networks as tools for assessing the efficiency of decision making units. Journal of Operational Research Society, 47(8), 1000-1017. Cecchini, L., Venanzi, S., Pierri, A. & Chiorri, M. (2018). Environmental efficiency analysis and estimation of CO2 abatement costs in dairy cattle farms in Umbria (Italy): A SBM-DEA model with undesirable output. Journal of Cleaner Production, 197(1), 895-907. Cooper, W. W., Deng, H., Gu, B., Li, S. & Thrall., R. M. (2001). Using DEA to improve the management of congestion in Chinese industries (1981–1997). Socio-Economic Planning Sciences, 35(4), 227-242. Cooper, W. W., Seiford, L. M. & Zhu, J. (2011). Data Envelopment Analysis: History, Models and Interpretations. Handbook on Data Envelopment Analysis, US: Springer. Costa, A., & Markellos, R. N., (1997). Evaluating public transport efficiency with neural network models. Transportatior research, 5(5), 301-312. Enke, D., & Suraphan, T. (2005). The use of data mining and neural networks for forecasting stock market returns. Expert Systems with Applications, 29(4), 927-940. Hagan, M. T., Demuth, H. B. & Beal, M. (2002). Neural Network Design. Singapore: Thamson Asia Pte Ltd. Hejazi, R., Anvari Rostami, A. A. & Moghadasy, M. (2008). Total Productivity Analysis of Export Development Bank of Iran and Productivity Growth in Branches- A Data Envelopment Analysis Application. Journal of Industrial Management, 1(1), 39-50. (in Persian) Hillier, F. S. & Lieberman, G. J. (2013). Inventories Management and Forecasting (translated by Faezirad, M. A. & Haghighat, A. Trans.). Tehran, Termeh Pub. (in Persian) Jahanshahloo, G. R. & Khodabakhshi, M. (2004). Suitable combination of inputs for improving outputs in DEA with determining input congestion: Considering textile industry of China. Applied Mathematics and Computation, 151(1), 263-73. Kazemi, M., Monazam Ebrahimpour, S. & Ilbeigi, A. R. (2014). Evaluating the efficiency of Mashhad Municipalities by Data Envelopment Analysis. Journal of Urban Planning, 4(15), 113-132. (in Persian) Kheirkhah, A., Azadeh, A., Saberi, M., Azaron, A. & Shakouri, H. (2013). Improved estimation of electricity demand function by using of artificial neural network, principal component analysis and data envelopment analysis. Computers & Industrial Engineering, (64)1, 425-441. Kocadağlı, O. & Aşıkgil, B. (2014). Nonlinear time series forecasting with Bayesian neural networks. Expert Systems with Applications, 41(15), 6596-6610. Kwon, H. B. & Lee, J. (2015). Two-stage production modeling of large U.S. banks: A DEA-neural network approach. Expert Systems with Applications, 42(19), 6758-6766. Kwon, H.B. (2017) Exploring the predictive potential of artificial neural networks in conjunction with DEA in railroad performance modeling. International Journal of Production Economics, 183(A), 159-170. Liang, F. (2005). Bayesian neural networks for nonlinear time series forecasting. Statistics and Computing, 15(1), 13-29. MA, J. (2015). A two-stage DEA model considering shared inputs and free intermediate measures. Expert Systems with Applications, 42(9), 4339-4347. Neely, A.D., Gregory, M. & Platts, K. (1995). Performance measurement system design: a literature review and research agenda. International Journal of Operations & Production Management, 15(4), 80-116. Poldrugovac, K., Tekavcic, M. & Jankovic, S. (2016). Efficiency in the hotel industry: an empirical examination of the most influential factors. Economic Research-Ekonomska Istraživanja, 29(1), 583-597. Samoilenko, S. & Osei-Bryson, K. M. (2010) Determining sources of relative inefficiency in heterogeneous samples: Methodology using Cluster Analysis, DEA and Neural Networks. European Journal of Operational Research, 206(2), 479-487. Seifert, L. M. & Zhu, J. (1998). Identifying excesses and deficits in Chinese industrial productivity (1953–1990): a weighted data envelopment analysis approach. Omega, 26(2), 279-96. Shabanpour, H., Yousefi, S. & Farzipoor Saen, R. (2017). Forecasting efficiency of green suppliers by dynamic data envelopment analysis and artificial neural networks. Journal of Cleaner Production, 142(2), 1098-1107. Silva, D. A., Alves, G. A, de Mattos Neto, P. S. G. & Ferreira, T. A. E. (2014). Measurement of Fitness Function efficiency using Data Envelopment Analysis. Expert Systems with Applications, 41(16), 7147-7160. Tone, K. (2001). A slack-based measure of efficiency in date envelopment analysis. European Journal of Operational Research, 130(3), 498-509. Tsai, C.F. & Lu, Y.H. (2009). Customer churn prediction by hybrid neural networks. Expert Systems with Applications, 36(10), 12547-12553. Zeng, Y., Zeng, Y., Choi, B., & Wang, L. (2017). Multifactor-influenced energy consumption forecasting using enhanced back-propagation neural network. Energy, 127, 381-396.