A New Hybrid Methodology Based on Data Envelopment Analysis and Neural Network for Optimization of Performance Evaluation
محورهای موضوعی : مجله بین المللی ریاضیات صنعتیA. Namakin 1 , S. E. Najafi 2 , M. Fallah 3 , M. Javadi 4
1 - Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
2 - Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
3 - Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
4 - Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
کلید واژه: Efficiencyو Linear Programming, Levenberg–Marquardt (LM), Artificial Neural Network, Data Envelopment Analysis,
چکیده مقاله :
In this paper, a new method of combining ANN and DEA (ANN-DEA) presented in which the input and output values for a large number of DMUs determined as neural network inputs. We have also compared the new model with the existing approach of ANN-DEA. To illustrate the ability of the proposed methodology some case studies are used, including a set of 500 Iranian bank branches.
در این مقاله ، یک روش جدید ترکیبی از شبکه های عصبی پرسپترون چند لایه و تحلیل پوششی داده ها ارائه می شود که در آن مقادیر ورودی و خروجی برای تعداد زیادی واحد تصمیم گیرنده به عنوان ورودی های شبکه عصبی تعیین می شود. می توان دید که با بکارگیری شبکه عصبی برای حل مسائل تحلیل پوششی داده ها نیاز به حل مدل مورد نظر برای هر واحد تصمیم گیرنده نیست و لذا الگوریتم ارائه شده زمان پردازش و استفاده از حافظه را نسبت به آنچه مورد نیاز روش متعارف در تحلیل پوششی داده ها است، به مقدار زیادی کاهش می دهد.جهت بررسی دقت شبکه ارائه شده، چندمطالعه موردی از جمله مجموعه ای از 500شعبه بانک مورد استفاده قرار می گیرد.نتایج نشان دهنده دقت بالا وزمان محاسباتی کمتر(اعتبارلازم) مدل ترکیبی پیشنهادی است.
[1] A. Charnes, W. W. Cooper, E. Rhodes, Measuring the efficiency of decision making units, European Journal of Operational Research 2 (1978) 429-444.
[2] R. D. Banker, A. Charnes, W. W. Cooper, Some models for estimating technical and scale inefficiencies in data envelopment analysis, Management Science 30 (1984) 1078-1092.
[3] J. Zhu, Quantitative models for performance evaluation and benchmarking: data envelopment analysis with spreadsheets, (2014).
[4] F. R. Roodposhti, F. H. Lotfi, M. V. Ghasemi, Acquiring targets in balanced scorecard method by data envelopment analysis technique and its application in commercial banks, Applied Mathematical Sciences 4 (2010) 3549-3563.
[5] Y. J. Lee, S. J. Joo, H. G. Park, An application of data envelopment analysis for Korean banks with negative data, Benchmarking: An International Journal 24 (2017) 1052-1064.
[6] H. Jiang, Y. He, Applying data envelopment analysis in measuring the efficiency of Chinese listed banks in the context of Macroprudential framework, Mathematics 6 (2018) 184.
[7] S. K. Lee, G. Mogi, K. S. Hui, A fuzzy analytic hierarchy process (AHP)/data envelopment analysis (DEA) hybrid model for efficiently allocating energy R&D resources: In the case of energy technologies against high oil prices, Renewable and Sustainable Energy Reviews 21 (2013) 347-355.
[8] E. Karasakal, P. Aker, A multicriteria sorting approach based on data envelopment analysis for R&D project selection problem,Omega 73 (2017) 79-92.
[9] A. R. Bahari, A. Emrouznejad, Influential DMUs and outlier detection in data envelopment analysis with an application to health care, Annals of Operations Research 223 (2014) 95-108.
[10] R. Lacko, Z. Hajduov, V. Gbor, Data envelopment analysis of selected specialized health centres and possibilities of its application in the terms of Slovak republic health care system, Journal of Health Management 19 (2017) 144-158.
[11] T. Ertay, D. Ruan, U. R. Tuzkaya, Integrating data envelopment analysis and analytic hierarchy for the facility layout design in manufacturing systems, Information Sciences 176 (2016) 237-262.
[12] E. Dzakn, H. Dzakn, Measuring the performance of manufacturing firms with super slacks based model of data envelopment analysis: An application of 500 major industrial enterprises in Turkey, European Journal of Operational Research 182 (2007) 1412-1432.
[13] F. H. Lotfi, M. V. Ghasemi, Malmquist productivity index on interval data in telecommunication firms, application of data envelopment analysis, Applied Mathematical Sciences 1 (2007) 711-722.
[14] M. Shafiee, F. H. Lotfi, H. Saleh, Supply chain performance evaluation with data envelopment analysis and balanced scorecard approach, Applied Mathematical Modelling 38 (2014) 5092-5112.
[15] S. Soheilirad, K. Govindan, A. Mardani, E. K. Zavadskas, M. Nilashi, N. Zakuan, Application of data envelopment analysis models in supply chain management: A systematic review and meta-analysi, Annals of Operations Research (2017) 1-5.
[16] I. Dobos, G. Vrsmarty, Inventory-related costs in green supplier selection problems with Data Envelopment Analysis (DEA), International Journal of Production Economics (2018).
[17] C. W. Huang, Assessing the performance of tourism supply chains by using the hybrid network data envelopment analysis model, Tourism Management 65 (2018) 303-316.
[18] A. Azadeh, S. F. Ghaderi, H. Omrani, H. Eivazy, An integrated DEACOLSSFA algorithm for optimization and policy making of electricity distribution units, Energy Policy 37 (2019) 2605-2618.
[19] A. D. Athanassopoulos, S. P. Curram, A comparison of data envelopment analysis and artificial neural networks as tools for assessing the efficiency of decision making units, Journal of the Operational Research Society 47 (1996) 1000-1016.
[20] A. Costa, R. N. Markellos, Evaluating public transport efficiency with neural network models,Transportation Research Part C: Emerging Technologies 5 (1997) 301-312.
[21] . C. Pendharkar, J. A. Rodger, Technical efficiency-based selection of learning cases to improve forecasting accuracy of neural networks under monotonicity assumption, Decision Support Systems 36 (2003) 117-136.
[22] D. Santin, F. J. Delgado, A. Valino, The measurement of technical efficiency: a neural network approach, Applied Economics 36 (2004) 627-635.
[23] F. J. Delgado, Measuring efficiency with neural networks, an application to the public sector, Economics Bulletin 3 (2005) 1-10.
[24] D. D. Wu, Z. Yang, L. Liang, Using DEAneural network approach to evaluate branch efficiency of a large Canadian bank, Expert Systems with Applications 31 (2006) 108-115.
[25] M. M. Mostafa, Modeling the efficiency of top Arab banks: A DEAneural network approach, Expert Systems with Applications 36 (2009) 309-320.
[26] D. elebi, D. Bayraktar, An integrated neural network and data envelopment analysis for supplier evaluation under incomplete information, Expert Systems with Applications 35 (2008) 1698-1710.
[27] A. Emrouznejad, E. Shale, A combined neural network and DEA for measuring efficiency of large scale datasets,Computers & Industrial Engineering 56 (2009) 249-254.
[28] H. B. Kwon, J. Lee, Two-stage production modeling of large US banks: A DEA-neural network approach, Expert Systems with Applications 42 (2015) 6758-6766.
[29] H. Shabanpour, S. Yousefi, R. F. Saen, Forecasting efficiency of green suppliers by dynamic data envelopment analysis and artificial neural networks, Journal of Cleaner Production 142 (2017) 1098-1107.
[30] A. Vaninsky, Combining data envelopment analysis with neural networks: Application to analysis of stock prices, Journal of Information and Optimization Sciences 25 (2014) 589-611.
[31] S. C. Hu, Y. K. Chung, Y. S. Chen, Using Hopfield neural networks to solve DEA problems, in Cybernetics and Intelligent Systems, 2008 IEEE Conference, IEEE.
[32] A. Azadeh, S. F. Ghaderi, M. Anvari, M. Saberi, H. Izadbakhsh, An integrated artificial neural network and fuzzy clustering algorithm for performance assessment of decision making units, Applied Mathematics and Computation 187 (2007) 584-599.
[33] A. Azadeh, L. Javanmardi, M. Saberi, The impact of decision-making units features on efficiency by integration of data envelopment analysis, artificial neural network, fuzzy Cmeans and analysis of variance, International Journal of Operational Research 7 (2010) 387-411.
[34] H. Liao, B. Wang, T. Weyman-Jones, Neural network based models for efficiency frontier analysis: an application to East Asian economies Growth decomposition, Global Economic Review 36 (2007) 361-384.
[35] D. Santin, On the approximation of production functions: a comparison of artificial neural networks frontiers and efficiency techniques, Applied Economics Letters 15 (2008) 597-600.
[36] S. Samoilenko, K. M. Osei-Bryson, Determining sources of relative inefficiency in heterogeneous samples: methodology using cluster analysis, DEA and neural networks, European Journal of Operational Research 206 (2010) 479-487.
[37] H. H. Liu, T. Y. Chen, Y. H. Chiu, F. H. Kuo, A comparison of three-stage DEA and artificial neural network on the operational efficiency of semi-conductor firms in Taiwan, Modern Economy 4 (2013) 20-31.
[38] H. B. Kwon, Performance modeling of mobile phone providers: A DEA-ANN combined approach, Benchmarking: An International Journal 21 (2014) 1120-1144.
[39] H. B. Kwon, J. Lee, J. J. Roh, Best performance modeling using complementary DEA-ANN approach: application to Japanese electronics manufacturing firms, Benchmarking: An International Journal 23 (2016) 704-721.
[40] H. B. Kwon, J. H. Marvel, J. J. Roh, J. J. Three-stage performance modeling using DEABPNN for better practice benchmarking, Expert Systems with Applications 71 (2017) 429-441.
[41] M. Toloo, A. Zandi, A. Emrouznejad, Evaluation efficiency of large-scale data set with negative data: an artificial neural network approach, The Journal of Supercomputing 71 (2015) 2397-2411.
[42] N. Misiunas, A. Oztekin, Y. Chen, K. Chandra, DEANN: A healthcare analytic methodology of data envelopment analysis and artificial neural networks for the prediction of organ recipient functional status,Omega 58 (2016) 46-54.
[43] E. Shokrollahpour, F. H. Lotfi, M. Zandieh, An integrated data envelopment analysisartificial neural network approach for benchmarking of bank branches, Journal of Industrial Engineering International 12 (2016) 137-143.
[44] S. Agarwal, DEA-neural networks approach to assess the performance of public transport sector of India, Opsearch 53 (2016) 248-258.
[45] M. Sharifi, J. Rezaeian, Efficiency evaluation of Mazandaran industrial parks by using neuro-DEA approach, International Journal of Industrial and Systems Engineering 23 (2016) 111-123.
[46] N. Singh, M. Pant, Evaluating the Efficiency of Higher Secondary Education State Boards in India: A DEA-ANN approach, in International Conference on Intelligent Systems Design and Applications, 2017, Springer, Cham.
[47] R. D. Raut, S. S. Kamble, M. G. Kharat, H. Joshi, C. Singhal, S. J. Kamble, A hybrid approach using data envelopment analysis and artificial neural network for optimising 3PL supplier selection, International Journal of Logistics Systems and Management 26 (2017) 203-223.
[48] A. Cavallin, M. Frutos, H. P. Vigier, D. G. Rossit, An Integrated model of data envelopment analysis and artificial neural networks for improving efficiency in the municipal solid waste management, in Handbook of Research on Emergent Applications of Optimization Algorithms, 2018, IGI Global.
[49] C. C. Lee, C. Ou-Yang, A neural networks approach for forecasting the suppliers bid prices in supplier selection negotiation process, Expert Systems with Applications 36 (2009) 2961-2970.
[50] M. A. Razi, K. Athappilly, A comparative predictive analysis of neural networks (NNs), nonlinear regression and classification and regression tree (CART) models, Expert Systems with Applications 29 (2005) 65-74.
[51] D. E. Rumelhart, G. E. Hinton, R. J. Williams, Learning representations by backpropagating errors, Nature 323 (1986) 533-560.
[52] M. I. Lourakis, A brief description of the Levenberg-Marquardt algorithm imple mented by levmar, Foundation of Research and Technology 4 (2005) 1-6.
[53] H. R. Ansari, M. J. Zarei, S. Sabbaghi, P. Keshavarz, A new comprehensive model for relative viscosity of various nanofluids using feed-forward back-propagation MLP neural networks, International Communications in Heat and Mass Transfer 91 (2018) 158-164.
[54] R. Hecht-Nielsen, Kolmogorov’s mapping neural network existence theorem, in Proceedings of the IEEE International Conference on Neural Networks III, 1987, IEEE Press.
[55] K. Hornik, M. Stinchcombe, H. White, Multilayer feedforward networks are universal approximators, Neural Networks 2 (1989) 359-366.
[56] G. Cybenko, Approximation by superpositions of a sigmoidal function, Mathematics of Control, Signals and Systems 2 (1989) 303-314.
[57] K. I. Funahashi, On the approximate realization of continuous mappings by neural networks,Neural networks 2 (1989) 183-192.
[58] R. Hecht-Nielsen, Theory of the backpropagation neural network, in Neural Networks for Perception, 1992.
