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An Efficiency Measurement and Benchmarking Model Based on Tobit Regression, GANN-DEA and PSOGA | ||
International Journal of Finance & Managerial Accounting | ||
مقاله 7، دوره 3، شماره 12، فروردین 2019، صفحه 79-93 اصل مقاله (780.33 K) | ||
نوع مقاله: Original Article | ||
نویسندگان | ||
Mohammad Reza Mirzaei1؛ Mohammad Ali Afshar Kazemi 2؛ Abbas Toloie Eshlaghy3 | ||
1Ph.D. Candidate, Department of industrial management, Central Tehran Branch, Islamic Azad University, Tehran, Iran. | ||
2Associated professor, Department of industrial management, Central Tehran Branch, Islamic Azad University, Tehran, Iran (Corresponding author). | ||
3Professor, Department of Industrial Management, Science and Research Branch, Islamic Azad University, Tehran, Iran. | ||
چکیده | ||
The purpose of this study is designing a model based on Tobit regression, DEA, Artificial Neural Network, Genetic Algorithm and Particle Swarm Optimization to evaluate the efficiency and also benchmarking the efficient and inefficient units. This model has three stages, and it uses the data envelopment analysis combined model with neural network, optimized by genetic algorithm, to evaluate the relative efficiency of 16 regional electric companies of Tavanir. A two-staged approach of data envelopment analysis and Tobit regression has been used to measure the effects of environmental variables on the mean efficiency of companies. Finally we use a hybrid model of particle swarm algorithm and genetic algorithm to benchmark the efficient and inefficient units. The mean efficiency of regional electric companies have increased from 0.8934 to 0.9147, during 2012 to 2017, and regional electric companies of Azarbayjan, Isfahan, Tehran, Khorasan, Semnan, Kerman, Gilan and Yazd, had the highest mean efficiency of 1, and west regional electric companies and Fars had the lowest efficiency of 0.7047 and 0.6025, respectively. | ||
کلیدواژهها | ||
Benchmarking؛ Efficiency؛ GANN-DEA؛ PSOGA؛ Tobit regression | ||
مراجع | ||
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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. | ||
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