An approach to rank efficient DMUs in DEA based on combining Manhattan and infinity norms
محورهای موضوعی : Data Envelopment Analysis
شکرالله زیاری
1
(گروه ریاضی دانشگاه آزاد اسلامی واحد فیروزکوه. ایران)
مناف شریف زاده
2
(گروه کامپیوتر دانشگاه آزاد اسلامی واحد فیروزکوه ایران)
کلید واژه: Data Envelopment Analysis (DEA), Ranking, Efficiency, Extreme efficient,
چکیده مقاله :
In many applications, discrimination among decision making units (DMUs) is a problematic technical task procedure to decision makers in data envelopment analysis (DEA). The DEA models unable to discriminate between extremely efficient DMUs. Hence, there is a growing interest in improving discrimination power in DEA yet. The aim of this paper is ranking extreme efficient DMUs in DEA based on exploiting the leave-one out idea and combining of Manhattan and infinity norms with constant and variable returns to scale. The proposed method has been able to overcome the existing difficulties in some ranking methods.
در بسیاری از برنامه های کاربردی، تبعیض در میان واحدهای تصمیم گیرنده (DMU) ها یک روش کار فنی مشکل ساز برای تصمیم گیرندگان در تحلیل پوششی داده ها (DEA) است. مدل های DEA قادر به تمایز قائل شدن میان DMU های به شدت کارآمد نمی باشد. از این رو، علاقه رو به رشد در بهبود قدرت تبعیض در DEA هنوز وجود دارد. هدف از این مقاله رتبه بندی DMU های بسیار کارآ در تحلیل پوششی داده ها ها بر اساس بهره برداری از یک ایده و ترکیب منهتن و نورم بی نهایت با بازده به مقیاس ثابت و متغیر است. روش پیشنهادی در برخی از روش های رتبه بندی قادر به غلبه بر مشکلات موجود است.
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