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

Mehdi Toloo*, Ameneh Zandi, Ali Emrouznejad

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)


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 languageEnglish
Pages (from-to)2397-2411
Number of pages15
JournalJournal of Supercomputing
Issue number7
Publication statusPublished - Jul 29 2015
Externally publishedYes


  • Artificial neural network
  • Data envelopment analysis (DEA)
  • LM-DEA
  • Levenberg–Marquardt (LM)
  • Negative data

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Information Systems
  • Hardware and Architecture

Cite this