Designing a Recommendation Model Based on Tobit Regression, GANN-DEA and PSOGA to Evaluate Efficiency and Benchmark Efficient and Inefficient Units

Document Type : Research Paper

Authors

1 Department of Industrial Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran

2 Department of Industrial Management, Science and Research Branch, Islamic Azad University, Tehran, Iran

Abstract

The main purpose of this study is to design a privatized proposal model for Tavanir regional electricity distribution and transmission companies. This proposed model is based on Tobit regression, GANN-DEA and PSOGA to evaluate the efficiency and modeling of efficient and inefficient units. This three-step process is benefited a hybrid data envelopment analysis model with a neural network optimized by a genetic algorithm to evaluate the relative efficiency of 16 Tavanir regional electricity companies. To measure the effect of environmental variables on the average efficiency of companies, two-stage data envelopment analysis and Tobit regression were used. Finally, with a hybrid model of particle mass algorithm and genetic algorithm, we have modeled for efficient and inefficient units. The average of efficiency of regional electricity companies during the years 2012 to 2017 has increased from 0.8934 to 0.9147. And companies in regions 1, 2, 4, 5, 8, 12, 13 and 16 have always had the highest efficiency average (one). And the power companies in regions 10 and 11 with the average efficiency values of 0.7047 and 0.6025 had the lowest efficiency values.

Keywords

References

1] Mehregan, M .(2004). Quantitative models in evaluating organizations performance (DEA). Chapter 2 and 3, Tehran University Press Faculty Publishing.
[2] 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 Vol, 45, No.1.
[3] Rezaeian, J., & Asgarinezhad, A. (2014). Performance Evaluation of Mazandaran Water and Wastewater by Data Envelopment Analysis and Artificial Neural Network, Journal of Industrial Engineering Vol, 48, No.2.
[4] Toloie-Eshlaghy, A., Alborzi, M., & Ghafari, B. (2012). Assessment of the personnel’s efficiency with Neuro/DEA combined model. Elixir Mgmt. Arts Vol. 43, No.1. PP.6605-6617.
[5] Shokrollahpour, E., Lotfi, F. H., & Zandieh, M. (2016). An integrated data envelopment analysis–artificial neural network approach for benchmarking of bank branches. Journal of Industrial Engineering International, 12(2), 137-143.
[6] Çelen, A. (2013). Efficiency and productivity (TFP) of the Turkish electricity distribution companies: An application of two-stage (DEA&Tobit) analysis. Energy Policy, 63, 300-310.
[7] Wu, Y., Hu, Y., Xiao, X., & Mao, C. (2016). Efficiency assessment of wind farms in China using two-stage data envelopment analysis. Energy Conversion and Management, 123, 46-55.
[8] Hjalmarsson, L., & Veiderpass, A. (1992). Efficiency and ownership in Swedish electricity retail distribution. International Applications of Productivity and Efficiency Analysis (pp. 3-19): Springer.
[9] Bagdadioglu, N., Price, C. M. W., & Weyman-Jones, T. G. (1996). Efficiency and ownership in electricity distribution: a non-parametric model of the Turkish experience. Energy Economics, 18(1-2), 1-23.
[10] Pérez-Reyes, R., & Tovar, B. (2009). Measuring efficiency and productivity change (PTF) in the Peruvian electricity distribution companies after reforms. Energy Policy, 37(6), 2249-2261.
[11] Hattori, T., Jamasb, T., & Pollitt, M. G. (2003). A comparison of UK and Japanese electricity distribution performance 1985-1998: lessons for incentive regulation.
[12] Hess, B., & Cullmann, A. (2007). Efficiency analysis of East and West German electricity distribution companies–Do the “Ossis” really beat the “Wessis”? Utilities Policy, 15(3), 206-214.
[13] Goto, M., & Tsutsui, M. (2008). Technical efficiency and impacts of deregulation: An analysis of three functions in US electric power utilities during the period from 1992 through 2000. Energy Economics, 30(1), 15-38.
[14] Bongo, M. F., Ocampo, L. A., Magallano, Y. A. D., Manaban, G. A., & Ramos, E. K. F. (2018). Input–output performance efficiency measurement of an electricity distribution utility using super-efficiency data envelopment analysis. Soft Computing. doi:10.1007/s00500-018-3007-2
[15] Meibodi, A. E. (1998). Efficiency considerations in the electricity supply industry: The case of Iran .Chapter 1, university of Surrey.
[16] Sadjadi, S., & Omrani, H. (2008). Data envelopment analysis with uncertain data: An application for Iranian electricity distribution companies. Energy Policy, 36(11), 4247-4254.
[17] Fallahi, M., & Ahmadi, V. (2005). Cost efficiency analysis of electricity distribution companies in Iran. Journal of Economic Researches, 71, 297-320.
[18] Russell, R. R. (1985). Measures of technical efficiency. Journal of Economic Theory, 35(1), 109-126.
[19] Charnes, A., Cooper, W., Lewin, A., & Seiford, L. (1995). Data Envelopment Analysis: Theory, Methodology and Applications, Kluwer Publications.
[20] Tone, K. (2001). A slacks-based measure of efficiency in data envelopment analysis. European Journal of Operational Research, 130(3), 498-509.
[21] Tobin, J. (1958). Estimation of Relationships for Limited Dependent Variables. Econometrica, 26(1), 24-36.
[22] Olatubi, W. and Dismukes D. (2000), “A Data Envelopment Analysis of the Levels and Determinants of Coal-fired Electric Power Generation Performance”, Utilities Policy, 9, PP. 47–59.
[23] Dreyfus, G. (2005). Neural networks: methodology and applications: Springer Science & Business Media.
[24] Alborzi, Mahmood. (2014). Translated of neural computing: an introduction-R Beale & T Jackson. Chapter 4, Institute of Scientific Publications of Sharif University of Technology: Tehran.
[25] Athanassopoulos, A. D., & Curram, S. P. (1996). 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(8), 1000-1016.
[26] Costa, Á., & Markellos, R. N. (1997). Evaluating public transport efficiency with neural network models. Transportation Research Part C: Emerging Technologies, 5(5), 301-312.
[27] Wu, D. D., Yang, Z., & Liang, L. (2006). Using DEA-neural network approach to evaluate branch efficiency of a large Canadian bank. Expert systems with applications, 31(1), 108-115.
[28] 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.
[29] Alborzi, Mahmood. (2014). Genetic algorithm. Chapter 3-5, Institute of Scientific Publications of Sharif University of Technology: Tehran.
[30] Angeline, P. J. (1998). Using selection to improve particle swarm optimization. Paper presented at the Evolutionary Computation Proceedings, 1998. IEEE World Congress on Computational Intelligence., The 1998 IEEE International Conference on.
[31] Garg, H. (2016). A hybrid PSO-GA algorithm for constrained optimization problems. Applied Mathematics and Computation, 274, 292-305.
[32] Toloie Ashlaghi, A, Afshar Kazemi, M.A. And Abbasi, F. (2013) Evaluation of the performance of insurance companies based on a balanced scorecard card and data envelopment analysis technique and providing a development path for inefficient companies. Journal of Business Management (17), 65-82.
[33] Azar, A. Daneshvar, M. Khodadad Hosseiny, S. H. Azizi, S.(2012). Designing a multilevel performance evaluation model: A robust data envelopment analysis approach. Enterprise Resource Management Research, Volume II (3), 1-22.
[34] Cook WD and Green RH.( 2005). Evaluating power company efficiency: a hierarchical model. Computers & Operations Research; 32: 813-823.
Advances in Industrial Engineering
Volume 53, Issue 3
July 2019
Pages 15-30

Files

Share

How to cite

Statistics