Abstract
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.
Original language | English |
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Pages (from-to) | 2397-2411 |
Number of pages | 15 |
Journal | Journal of Supercomputing |
Volume | 71 |
Issue number | 7 |
DOIs | |
Publication status | Published - Jul 29 2015 |
Externally published | Yes |
Keywords
- Artificial neural network
- Data envelopment analysis (DEA)
- LM-DEA
- Levenberg–Marquardt (LM)
- Negative data
- SORM-DEA
ASJC Scopus subject areas
- Software
- Theoretical Computer Science
- Information Systems
- Hardware and Architecture