ارزیابی کارایی شرکت های برق منطقه ای ایران با استفاده از تحلیل پوششی داده ها و شبکه های عصبی

- بنیادی نائینی، علی، یوسفی، سعید و فائضی‌راد، محمدعلی، (1395)، پویاسازی خوشه‌بندی مشتریان با استفاده از روش DEA-DA در بستر شبکه عصبی مصنوعی SOM، فصلنامه علمی- پژوهشی مطالعات مدیریت صنعتی، سال چهاردهم، شماره 40، صفحات 165-187.

2- [1] رمضانیان، محمد رحیم، اویسی عمران، اکرم و یاکیده، کیخسرو، (1391)، تبیین الگوی ارزیابی عملکرد در طی زمان با تحلیل پنجره‌ای، مدیریت صنعتی تهران، دوره 4: 2، 69-86.

3- Angelidis, D., Lyroudi, K., (2006), Efficiency in the Italian banking industry: Data envelopment analysis and neural networks. International Research Journal of Finance and Economics 1 (5), 155–165.

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.

5- Azadeh, A., Saberi, M., Tavakkoli Moghaddam, R., (2011), Javanmardi, L., An integrated Data Envelopment Analysis–Artificial Neural Network–Rough Set Algorithm for assessment of personnel efficiency, Expert Systems with Applications, (38), pp. 1364–1373.

6- Banker, R.D., (1993). Maximum likelihood, consistency and data envelopment analysis: A statistical foundation. Management Science, 39 (10), 1265–1273.

7- Banker, R.D., (1996). Hypothesis tests using data envelopment analysis. Journal of Productivity Analysis, 7 (23), 139–159.

8- Boussofiane, A., Dyson, R.G., Thanassoulis, E., (1991). Applied data envelopment analysis. European Journal of Operational Research, 52 (1), 1–15.

9- Costa, A., Markellos, R.N., 1997. Evaluating public transport efficiency with neural network models. Transportation Research 5 (5), 301–312.

10- Emrouznejad, A., Shale, E. A., (2009), a combined neural network and DEA for measuring efficiency of large scale data sets.  Computers and Industrial Engineering 56, 249–254.

11- Golany, B., Roll, Y., (1989). An application procedure for DEA. Omega, 17(3), 237–250.

12- Jenkins, L., Anderson, M., (2003). A multivariate statistical approach to reducing the number of variables in data envelopment analysis. European Journal of Operational Research, 147 (1), 51–61.

13- Klimberg, R., Puddicombe, M., (1995). A multiple objective approach to data envelopment analysis, working paper 95-105, School of Management, Boston University, MA.

14- Kwon, H., B., and Lee, J., (2015), Two-stage production modeling of large U.S. banks: a DEA-neural network approach, Expert Systems with Applications, Vol, (42), Issue (19), pp. 6758- 6766.

15- Kwon, H., B., (2017), Exploring the predictive potential of artificial neural networks in conjunction with DEA in railroad performance modeling, Int. J. Production Economics, 183, pp. 159–170.

16- Kwon, H., B., Marvel, J., H., and Roh, J., J., (2017), Three-stage performance modeling using DEA-BPNN for better practice benchmarking, Expert Systems with Applications, 71, pp. 429-441.

17- Ming-Chun, T., Shu-Ping, L., Ching-Chan, C., Yen-Ping, L., (2009), The consumer loan default predicting model – An application of DEA–DA and neural network, Expert Systems with Applications 36, pp. 11682–11690.

18- Misiunas, N., Oztekin, A., Chen, Y. and Chandra, k., (2016), DEANN: A healthcare analytic methodology of data envelopment analysis and artificial neural networks for the prediction of organ recipient functional status, Vol, 58, pp. 46-58.

19- Nunamaker, T.R., (1985). Using data envelopment analysis to measure the leniency of non-profit organizations: A critical evaluation. Managerial and Decision Economics, 6 (1), 50–58.

20- Olanrewaju, O. A., Jimoh, A. A. & Kholopane, P. A., (2016), assessing the energy potential in the South African industry: A combined IDA-ANN-DEA (Index Decomposition Analysis-Artificial Neural Network-Data Envelopment Analysis) model, Energy, (63), pp. 225- 232.

21- Pendharkar, P., Rodger, J., 2003. Technical efficiency-based selection of learning cases to improve forecasting accuracy of neural networks under monotonicity assumption. Decision Support Systems 36 (1), 117–136.

22- Ramezanian, M. R., Oveyssi Omran, A., and Yakideh, K. (2012). Explanation of Performance Evaluation Model over Time by Window Analysis. Industrial Management, 4 (2), 69-86. (In persian)

23- Salinas-Jimenez, J., Smith, P., (1996). Data envelopment analysis applied to quality in primary health care. Annals of Operations Research, 67, 141–161.

24- Shabanpour, H., Yousefi, S. & Farzipoor Saen, R., (2016), Forecasting efficiency of green suppliers by dynamic data envelopment analysis and artificial neural networks, In Press, pp. 1-10.

25- Saghafi, H., and Arabloo, M., (2017), Modeling of CO 2 solubility in MEA, DEA, TEA, and MDEA aqueous solutions using AdaBoost-Decision Tree and Artificial Neural Network, International Journal of Greenhouse Gas Control, 58, pp. 256–265.

26- Sharifian, A., & Sharifian, S., (2015), A new power system transient stability assessment method based on Type-2 fuzzy neural network estimation, Electrical Power and Energy Systems 64, pp. 71–87.

27- Vlontzos, G., and Pardalos, P. M., (2017), Assess and prognosticate green house gas emissions from agricultural production of EU countries, by implementing, DEA Window analysis and artificial neural networks, Renewable and Sustainable Energy Reviews, 76, pp. 155–162.

28- Wang, S., (2003), Adaptive non-parametric efficiency frontier analysis: A neural- network-based model. Computers and Operations Research 30 (2), 279 – 295.

29- Wu, D., (2009), Supplier selection: A hybrid model using DEA, decision tree and neural network. Expert Systems with Applications 36 (5), 9105–9112.

30- Yong-Ming, H., Zhi-Qiang, G. & Qun-Xiong, Z., (2016), Energy optimization and prediction of complex petrochemical industries using an improved artificial neural network approach integrating data envelopment analysis, Energy Conversion and Management, 124, pp. 73–83.