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Assessment of Progress and Regression of Iran’s Regional Electric Companies: Extended Network SBM Model

Document Type : Original Article

Authors

1 Faculty of Engineering, Islamic Azad University, Central Tehran Branch, Tehran, Iran.

2 Department of Basic Science, Science and Research Branch, Islamic Azad University, Tehran, Iran.

3 Department of Mothematics , Shahr-e-Qods Branch , Islamic Azad University, Tehran, Iran.

Abstract

Assessment of progress and regression of organizations is necessary to determine their performance and determination of the efficacy of managerial decisions, supply usage, and weak and strong points for the senior managers and decision-makers. They can improve the efficiency of the units based on this assessment. In this paper, using data envelopment analysis, the performance of regional electric companies of Iran in 2015 and 2016 is assessed. Because of semi-positive and negative indexes, the Slack-Based Measure (SBM) model of efficiency is developed for the 16 regional electric companies of Iran. To determine progress and regression in 2016 compared with 2015, models are proposed to compute the indexes of productivity. Finally, solving the proposed models, the Malmquist productivity index is computed for regional electric companies of Iran with 18 input, intermediate and output indexes and considering the amount of production of renewable energies as one of the important output indexes because of the irrefutable necessity of this kind of energies in the world. Their progress and regression are obtained using the Gams software showing progress in two companies, and regression in 13 companies while one company had neither progress nor regression. Studies performed show that agility of the organizational structure, financial and human resource limitations, sanctions and imbalance between the actual price of production of one kilowatt-hour of electricity and its sale price are most effective factors on the progress and regression of the companies.

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References
[1] Charnes A. ، Cooper W.W., Rhodes E. ، Measuring the efficiency of decision making unit، European Journal of Operational Research، 2 (1978) 429- 444.
 
[2] Banker R. J. ، Charnes A., Cooper W. W., Some models for estimating technical and scale inefficiencies in data Analysis، Management Science، 30 (9) (1984) 1078-1092.
 
[3]Emrouznejad, A., Anouuze, A. Thanassoulis E., A semi-oriented radial measure to measuring the efficiency of decision making units with negative data, using DEA. European Journal of Operational Research، 200 (2010) 297-304.
 
[4] J. Sharp A., Liu W. B., Meng W. A.. Modified slack-based measure model for data envelopment analysis with “natural” negative outputs and inputs،  joper. Res. Soc، 57 (2006) 1-6.
 
[5] Tone K. A.. slack-based measure of efficiency in data envelopment analysis، Eur. J. Opr. Res. 130 (2001) 498-509. 
 
[6] Manthos D. Delis, Maria Iosifidi, Mike Tsionas, Management Estimation in Banking, European Journal of Operational Research, (2019), 1-47.
 
[7] Tom Van Puyenbroeck, ValentinaMontalto, Michaela Saisana, Benchmarking culture in Europe: a Data Envelopment Analysis approach to identify city-specific strengths,EJOR, 20, (2020),1-31.
 
[8] Dickson K. Gidion, Jin Hong, Magdalene Z. A. Adams, Mohammad Khoveyni, Network-DEA models for assessing urban water utility efficiency, Utilities Policy, 57,(2019), 48-58.
 
[9] Sebastián Lozano, SomayehKhezri,  Network DEA smallest improvement approach, Omega, In press, corrected proof Available online 11 October 2019.
 
[10] Xiaoyang Zhou, Ying Wang, Jian Chai, Liqin Wang, Benjamin Lev, Sustainable supply chain evaluation: A dynamic double frontier Network DEA model with interval type-2 fuzzy data, Information Sciences, December 2019, 1-41.
 
[11] Xiaoyang Zhou،RuiLuo, Liming Yao, Shan Cao،Shouyang Wang. “Assessing integrated water use and wastewater treatment systems in China: A mixed network structure two-stage SBM DEA model.
 
[12] Malmquist، S.، (1953). Index numbers and indifference surface. Trabajos de Estatistica، 4، 209-248.
 
[13] Li-li Ding , Liang Lei, Lei Wang ,Liang-fu Zhang, Assessing industrial circular economy performance and its dynamic evolution: An extended Malmquist index based on cooperative game network DEA, Science of the Total Environment, 731,(2020),1-13.
 
[14] Jesus T. Pastor , C. A. Knox Lovell , Juan Aparicio, Defining a new graph inefficiency measure for the proportional directional distance function and introducing a new Malmquist productivity index, EJOR, 281, ( 2020), Pages 222-230.
 
[15] Hongwei Liu, Ronglu Yang, Dongdong Wu, Zhixiang Zhou, Green productivity growth, and competition. analysis of road transportation at the provincial level employing Global Malmquist-Luenberger Index approach, Journal of Cleaner Production,  279, (2021), 1-44.
 
[16] Seyed Zaman Hoseini, Farhad HoseinzadehLotfi, Mahnaz Ahadzadeh Namin, Introduction of a model for assessing regional electric companiesin 2016, National conference of data envelop analysis, 11th, Shiraz, 2019.
 
[17] Mohammad Ali Jahantighi, Zohreh Moghadas, Mohsen Vaez Ghasemi, Multistage Malmquist productivity index, Journal of operational research and its applications, eighth year, 59-70, 2011.
 
Renewable Energy Research and Applications
Volume 6, Issue 2
July 2025
Pages 315-323
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