ارزیابی کارایی شرکت های برق منطقه ای ایران با استفاده از تحلیل پوششی داده ها و شبکه های عصبی

نوع مقاله: مقاله پژوهشی

نویسندگان

1 هیئت علمی و مدیر گروه مدیریت صنعتی دانشگاه سمنان

2 دانشگاه آزاد رشت

3 گروه مدیریت صنعتی، دانشگاه سمنان

4 دانشجوی دانشگاه آزاد رشت

چکیده

انتخاب متغیرهای ورودی و خروجی در تعیین نمرات کارایی تحلیل پوششی داده‌ها از اهمیت فراوانی برخوردار است. در این پژوهش با استفاده از شبکه عصبی مصنوعی به تعیین ورودی‌ها و خروجی‌های‌ شرکت‌های برق منطقه‌ای پرداخته شده است. کاربرد شبکه عصبی در انتخاب ورودی‌ها و خروجی‌های شرکت‌های برق منطقه‌ای امری است که در ادبیات موضوع سابقه نداشته و مزیت اصلی روش پیشنهادی محسوب می‌شود. به‌‌منظور آموزش شبکه عصبی دو لایه MLP، از روش آموزش پس از انتشار خطای ارتجاعی استفاده گردید؛ پس از آموزش شبکه عصبی، عملکرد شبکه عصبی با استفاده از الگوهای تست، مورد بررسی قرار گرفت. مقدار RMSE مریوط به 15 الگوی تست برابر 0269/0 به‌دست آمد که نشان‌دهنده دقت بالای شبکه آموزش داده شده است. تحلیل حساسیت پارامترهای مورد بررسی که همان ورودی‌ها و خروجی‌های تحلیل پوششی داده‌ها هستند، با افزایش ده درصدی پارامترها نسبت به حالت قبل از افزایش انجام شده و میانگین خطای نسبی خروجی برای پارامترهای شبکه عصبی محاسبه شده است. بر اساس میزان میانگین خطای نسبی خروجی، ورودی‌ها و خروجی‌های تحقیق مشخص گردید. مقایسه نمرات کارایی شرکت‌های برق منطقه‌ای قبل و بعد از کاهش تعداد متغیرها، تعداد شرکت‌های کارا در طی شش دوره زمانی فوق از 4/62 درصد به 4/26 درصد کاهش یافته است.

کلیدواژه‌ها


عنوان مقاله [English]

Inputs and Outputs Selection of Data Envelopment Analysis to Evaluate the Performance of Regional Electricity Companies in Iran by Neural Network

نویسنده [English]

  • Akram Oveysiomran 3
3 Ph.D candidate
چکیده [English]

Input and output selection in Data Envelopment Analysis (DEA) has many important. In this research, inputs and outputs of reginal power companies are selected with artifitial neural network. The application of neural network in the selection of inputs and outputs of reginal power companies is not a precedent in the literature and it is considered the main advantage of the proposed method. In order to train two layers MLP neural network, after presenting of error resilience, learning method was used. After neural network training, neural network performance is examined by using the test set. RMSE value for 15 test set equals 0/0269 which reflects the high accuracy of training network. The Sensitivity Analysis of the studied parameters which are the same inputs and outputs of Data Envelopment Analysis, with ten percent increase of parameter, compared to the prior one was carried out and output relative error average for neural network parameters was calculated. Based on the output relative error average, inputs and outputs were determined. By comparing the efficiency scores of regional electricity companies before and after reducing the number of variables, it is noticed that the number of efficient companies during the above four periods decreased from 50 percent to 11 percent. Finally, the neural network application in inputs and outputs selection of the regional electricity companies was unprecedented in the literature and this is the main advantage of this method.

کلیدواژه‌ها [English]

  • Input and Output Selection
  • Data Envelopment Analysis
  • Neural Network
  • Window Analysis
  • Regional Electricity Companies
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