طراحی یک مدل ترکیبی جدید مبتنی بر تحلیل پوششی داده‌ها، شبکه عصبی مصنوعی، الگوریتم ژنتیک و بهینه‌سازی انبوه ذرات برای ارزیابی کارایی و الگوسازی واحد‌های کارا و ناکارا

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری، واحد تهران مرکز، دانشگاه آزاد اسلامی.

2 دانشیار، واحد تهران مرکز، دانشگاه آزاد اسلامی.

3 استاد، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی.

چکیده

هدف اصلی پژوهش حاضر، طراحی یک مدل ترکیبی جدید مبتنی بر تحلیل پوششی داده­‌ها، شبکه عصبی مصنوعی، الگوریتم ژنتیک و بهینه‌سازی انبوه ذرات برای ارزیابی کارایی و الگوسازی واحد­های کارا و نا­کارا است که واحد­های تصمیم­‌گیری در آن اندک باشد. فرایندی دومرحله­‌ای، برای ارزیابی کارایی نسبی۱۶ شرکت‌های برق منطقه‌ای شرکت توانیر، از مدل ترکیبی تحلیل پوششی داده‌ها با شبکه عصبی که به‌وسیله الگوریتم ژنتیک بهینه شده است، بهره برده است وبا یک الگوریتم ترکیبی انبوه ذرات با الگوریتم ژنتیک به الگوسازی برای واحدهای کارا و ناکارا پرداخته شده است. میانگین کارائی شرکت‌های برق منطقه­‌ای طی سال‌های ۱۳۹۱ تا ۱۳۹۶ از ۰/۸۹۳۴ به ۰/۹۱۴۷ افزایش یافته است و شرکت‌های برق منطقه‌­ای آذربایجان، اصفهان، تهران، خراسان، سمنان، کرمان، گیلان و یزد، همواره دارای بیشترین میانگین کارائی، ۱ و شرکت­‌های برق منطقه‌ای غرب و فارس با مقادیر میانگین کارائی  ۰/۷۰۴۷ و ۰/۶۰۲۵ دارای کمترین مقدار کارائی طی سال‌های ۱۳۹۱ تا ۱۳۹۶ بوده‌اند. 

کلیدواژه‌ها

عنوان مقاله [English]

Designing a New Combined Model Based on Data Envelopment Analysis, Artificial Neural Network, Genetic Algorithm and Particle Swarm Optimization to Evaluate Efficiency and Benchmarking of Efficient and Inefficient Units

نویسندگان [English]

  • Mohammad Reza Mirzaei 1
  • Mohammad Ali Afshar Kazemi 2
  • Abbas Toloie Eshlaghy 3
1 Ph.D. Candidate, Central Tehran Branch, Islamic Azad University.
2 Associate Prof., Central Tehran Branch, Islamic Azad University.
3 Prof., Islamic Azad University, Tehran, Iran.
چکیده [English]

The main purpose of the present study is to design a new hybrid model based on data envelopment analysis, artificial neural network, genetic algorithm and particle swarm optimization to evaluate the efficiency and benchmarking of efficient and inefficient units. A two stage process has been used to evaluate the relative efficiency of 16 Tavanir regional power companies, using the combined model of data envelopment analysis with the neural network optimized by genetic algorithm and using a hybrid model of particle swarm optimization with genetic algorithm Benchmarking for efficient and inefficient units has been addressed. The average efficiency of regional power companies during the years 1391 to 1396 has increased from 0.8934 to 0.91477, while the regional power companies of Azerbaijan, Isfahan, Tehran, Khorasan, Semnan, Kerman, Gilan and Yazd have always had the highest average efficiency, And West and Fars regional electric companies with average values of 0.70747 and 0.60525 have the lowest efficiency during the years 1391 to 1396.

کلیدواژه‌ها [English]

  • Hybrid Particle Swarm Optimization - Genetic Algorithm
  • Benchmarking
  • Efficiency
  • Combined Model of Data Envelopment Analysis with Neural Network and Genetic

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