Evaluation efficiency of large-scale data set with negative data: an artificial neural network approach

Mehdi Toloo*, Ameneh Zandi, Ali Emrouznejad

*المؤلف المقابل لهذا العمل

نتاج البحث: المساهمة في مجلةمقالمراجعة النظراء

13 اقتباسات (Scopus)

ملخص

Data envelopment analysis (DEA) is the most widely used methods for measuring the efficiency and productivity of decision-making units (DMUs). The need for huge computer resources in terms of memory and CPU time in DEA is inevitable for a large-scale data set, especially with negative measures. In recent years, wide ranges of studies have been conducted in the area of artificial neural network and DEA combined methods. In this study, a supervised feed-forward neural network is proposed to evaluate the efficiency and productivity of large-scale data sets with negative values in contrast to the corresponding DEA method. Results indicate that the proposed network has some computational advantages over the corresponding DEA models; therefore, it can be considered as a useful tool for measuring the efficiency of DMUs with (large-scale) negative data.

اللغة الأصليةEnglish
الصفحات (من إلى)2397-2411
عدد الصفحات15
دوريةJournal of Supercomputing
مستوى الصوت71
رقم الإصدار7
المعرِّفات الرقمية للأشياء
حالة النشرPublished - يوليو 29 2015
منشور خارجيًانعم

ASJC Scopus subject areas

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