A robust cross-efficiency data envelopment analysis model with undesirable outputs

Madjid Tavana*, Mehdi Toloo, Nazila Aghayi, Aliasghar Arabmaldar

*المؤلف المقابل لهذا العمل

نتاج البحث: المساهمة في مجلةArticleمراجعة النظراء

24 اقتباسات (Scopus)

ملخص

Degenerate optimal weights and uncertain data are two challenging problems in conventional data envelopment analysis (DEA). Cross-efficiency and robust optimization are commonly used to handle such problems. We develop two DEA adaptations to rank decision-making units (DMUs) characterized by uncertain data and undesirable outputs. The first adaptation is an interval approach, where we propose lower- and upper-bounds for the efficiency scores and apply a robust cross-efficiency model to avoid problems of non-unique optimal weights and uncertain data. We initially use the proposed interval approach and categorize DMUs into fully efficient, efficient, and inefficient groups. The second adaptation is a robust approach, where we rank the DMUs, with a measure of cross-efficiency that extends the traditional classification of efficient and inefficient units. Results show that we can obtain higher discriminatory power and higher-ranking stability compared with the interval models. We present an example from the literature and a real-world application in the banking industry to demonstrate this capability.

اللغة الأصليةEnglish
رقم المقال114117
دوريةExpert Systems with Applications
مستوى الصوت167
المعرِّفات الرقمية للأشياء
حالة النشرPublished - أبريل 1 2021

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