A Hybrid model based on neural network and Data Envelopment Analysis model for Evaluation of unit Performance
الموضوعات :صادق حیدری 1 , احسان زنبوری 2 , حمید پروین 3
1 - دانش آموخته کارشناسی ارشد کامپیوتر، دانشگاه آزاد اسلامی، نورآباد ممسنی، ایران
2 - گروه علوم پایه، واحد نورآباد ممسنی، دانشگاه آزاد اسلامی، نورآباد ممسنی، ایران
3 - گروه کامپیوتر، واحد نورآباد ممسنی، دانشگاه آزاد اسلامی، نورآباد ممسنی، ایران
الکلمات المفتاحية: Data envelopment analysis, Efficiency, Performance Evaluation, Artificial Neural Network (ANNS), BCC Output Oriented Model,
ملخص المقالة :
Efficiency and evaluation is one of the main and most important demands of organizations, companies and institutions. As these organizations deal with a large amount of data, therefore, it is necessary to evaluate them on the basis of scientific methods to improve their efficiency. Data envelopment analysis is a suitable method for measuring the efficiency and performance of organizations. This paper has been conducted to evaluate the performance and efficiency of decision making units. First, using the data envelopment analysis, the BCC output oriented model, these units are ranked and the shortcoming of the model in terms of efficacy measurement and separation are determined. Then, to overcome such problems, a combined method of data envelopment analysis; the BCC output oriented model and artificial neural network are used to evaluate the efficiency of these units and finally the results of the two models are compared.Given the efficiency obtained with the BCC output oriented method, it was observed that the amount of efficiency for some units which leads for these units not to be ranked but using the proposed NEURO-DEA method, no two units have the same efficiency and given the obtained efficiency, these units can be evaluated and ranked.
Aslani, G., MOUMENI, M. S., Malek, A., & Ghorbani, F. (2009). Bank efficiency evaluation using a neural network-DEA method.. Iranian Journal of mathematical Sciences and information, 4(2), 33-48.
Azadeh, A., Saberi, M., Moghaddam, R. T., & Javanmardi, L. (2011). An integrated data envelopment analysis–artificial neural network–rough set algorithm for assessment of personnel efficiency. Expert Systems with Applications, 38(3), 1364-1373.
Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management science, 30(9), 1078-1092.
Charnes, a., cooper, w. W., rohdes, e. (1978). Measuring The Efficiency of Decision Making Units, European Journal of Operational Research, 2(6), 429-444.
Emrouznejad, A., & Shale, E. (2009). A combined neural network and DEA for measuring efficiency of large scale datasets. Computers & Industrial Engineering, 56(1), 249-254.
Farrell, M. J. (1957). The measurement of productive efficiency. Journal of the Royal Statistical Society. Series A (General), 120(3), 253-290.
Kordrostami, S., Amirteimoori, A., & Jahani Sayyad Noveiri, M. (2016). Ranking of bank branches with undesirable and fuzzy data: A DEA-based approach. Iranian Journal of Optimization, 8(2), 71-77.
Kwon, H. B., Lee, J., & Roh, J. J.(2009). Application of DEA-ANN for Best Performance Modeling.
Mehrabian, S., Hossein Mahtadi, S., & Hadi, A. (2011). "Evaluation of the Efficiency of Bank DMUes of Eghtesade E Novin by Combining Neural Network and Data Envelopment Analysis". Journal of Operations Research and its Applications, Eighth, 4, 29 -39.
Santin, D., Delgado, F. J., & Valino, A. (2004). The measurement of technical efficiency: a neural network approach. Applied Economics, 36(6), 627-635.
Yu, M. M., & Lin, E. T. (2008). Efficiency and effectiveness in railway performance using a multi-activity network DEA model. Omega, 36(6), 1005-1017.
Zandi, A., & Toloo, M. (2013). Estimation of the Efficiency of Branches of Bank Saderat Iran by Artificial Neural Network. Fourth National Conference on Data Envelopment Analysis, bipolar University of Mazandaran.
