ارزیابی بهره وری، کارایی و رتبه بندی نیروگاه های حرارتی: یک رویکرد مبتنی بر تحلیل پوششی داده های تصادفی
محورهای موضوعی : مدیریت بازرگانی- بازرگانی
1 - استادیار گروه مدیریت، واحد دهاقان، دانشگاه آزاد اسلامی، دهاقان، ایران
کلید واژه: تحلیل پوششی داده‌های تصادفی, کارایی متقاطع تصادفی, خروجی نامطلوب تصادفی, معیار رتبه‌بندی میانگین, اولویت رتبه‌بندی تصادفی ,
چکیده مقاله :
در تحلیل پوششی داده ها (DEA) مدل های مختلف در زمینه های گوناگون با داده های مختلف برای ارزیابی و رتبه بندی واحدهای تصمیم گیرنده (DMU) طراحی شده است. حال آنکه در بسیاری از مسائل کاربردی، مدیران واحدها با داده هایی تصادفی روبرو هستند و آنها برای ارزیابی واحدهای تحت نظارت خود به روشی نیاز دارند که بتواند اینگونه DMU ها را ارزیابی و رتبه بندی کنند. در کار کردن با داده‌های تصادفی با در نظر گرفتن احتمالی برای وقوع حالت های پیش بینی نشده (سطح خطا)، که از طرف مدیران ارائه می شود، DMU ها ارزیابی می شوند. در این مقاله با استفاده تکنیک های آمار و احتمالات و توزیع نرمال و مدلBCC دارای خروجی‌های نامطلوب و با در نظر گرفتن خطای مشخص یک مدل تصادفی جدید تحت عنوان معیار رتبه‌بندی میانگین جهت ارزیابی کارایی داده‌های تصادفی پیشنهاد می شود. بر اساس آن کارایی متقاطع تصادفی محاسبه گردیده است. از آنجایی که وزن های بهینه در ارزیابی کارایی متقاطع تصادفی منحصر به فرد نیستند برای رتبه‌بندی بهتر و اولویت دادن به آنها روش خودخواهانه پیشنهاد می شود. نهایتاً مدل های پیشنهاد شده برای 32 واحد نیروگاه حرارتی که دارای ورودی ها و خروجی های مطلوب و نامطلوب تصادفی هستند پیاده سازی شده است.
In data envelopment analysis, different models are developed in different fields with different data for evaluation and ranking of DMUs. While in many applications issues, unit managers are faced with stochastic data, and they need a method to evaluate their supervised units in a way that can evaluate and rank such DMUs. When working with stochastic data, considering the probability of occurrence of unpredictable states (the level of error) provided by managers, the DMUs are evaluated. In this paper using Probability statistics techniques and normal distribution and the BCC model with undesirable outputs and a specific risk ofSpecified,a new stochastic model called Expected Ranking Criterion is introduced. Based on this,the stochastic cross-efficiency evaluation. Given the non-uniqueness of resulting optimal solutions, a model is introduced for rating priorities by which cross-efficiency is performed using aggressive method. The proposed model is implemented for 32 thermal power plants with stochastic inputs and undesirable outputs.
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