ارتقای قدرت تفکیک‌پذیری در مدل تحلیل پوششی داده‌ها با استفاده از متغیرهای انحراف

نوع مقاله: مقاله علمی پژوهشی

نویسندگان

1 گروه مهندسی صنایع، دانشکده مهندسی، دانشگاه فناوری های نوین سبزوار، سبزوار، ایران

2 گروه علوم پایه، دانشگاه آزاد اسلامی واحد تهران غرب، تهران، ایران

چکیده

در چندین دهۀ گذشته تحلیل پوششی داده‌ها (DEA) به‌عنوان تکنیکی برای ارزیابی عملکرد و اندازه‌گیری کارایی واحدهای تصمیم‌گیری (DMU)، بر اساس داده‌های ورودی ـ خروجی آنها معرفی شد. با وجود این، نقصان و ضعف در قدرت تفکیک‌پذیری و عدم توزیع مناسب وزن‌ها به‌عنوان اشکالات عمده در DEA باقی مانده‌اند. در ادبیات موضوع، مدل‌هایی برای حل این مشکلات ارائه شده است که این مدل‌ها مشکلات دیگری از قبیل ناشدنی بودن دارند. در این مقاله با به‌کار بردن یکی از معیارها از مدل DEA چندمعیاره (MCDEA)که در اواخر دهۀ 1990 میلادی توسعه یافت، اضافه کردن کرانی پایین برای وزن‌ها و همچنین ارائۀ ابتکار و تکنیکی برای تفکیک و رتبه‌بندی همۀ واحدهای تصمیم‌گیری کارا، به‌دنبال برطرف کردن مشکلات اشاره‌شده هستیم. برای تست و سنجش قابلیت متد پیشنهادی در مقابل مدل‌های DEA موجود، به حل و تحلیل نتایج دو مثال عددی می‌پردازیم.
 

کلیدواژه‌ها


عنوان مقاله [English]

Improving Discrimination Power in Data Envelopment Analysis Using Deviation Variables

نویسندگان [English]

  • Mohammadreza Ghasemi 1
  • Arash Nabizadeh 2
1 Department of Industrial Engineering, Faculty of Engineering, Sabzevar University of New Technology, Sabzevar, Iran
2 Department of Statistics, Faculty of Sciences, Islamic Azad University West Tehran Branch, Tehran, Iran
چکیده [English]

Data Envelopment Analysis (DEA) has been proposed as a performance evaluative technique to measure the relative efficiency of decision-making units (DMUs) based on their respective multiple inputs and outputs. Lack of great discrimination power and poor weight dispersion has remained as the major issues in DEA. Hence, several methods were addressed in the literature as strategies to resolve the stated problems. However, there are some drawbacks to these methods too, which may lead to infeasible solutions. In order to address these drawbacks sufficiently, we extended the deviation variable form of classical DEA model by adding the lower bound to the input-output weights i.e. multi-criteria data envelopment analysis (MUDEA) developed in the late 1990s and proposed a procedure for ranking efficient units based on the deviation variables values framework. We further illustrated the performance of our proposed method against the alternative methods based on two numerical examples.
 

کلیدواژه‌ها [English]

  • Data Envelopment Analysis
  • Discrimination power
  • Ranking
  • Weights dispersion
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