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 journalArticle

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 languageEnglish
Pages (from-to)2397-2411
Number of pages15
JournalJournal of Supercomputing
Volume71
Issue number7
Early online date21 Feb 2015
DOIs
Publication statusPublished - Jul 2015

Bibliographical note

The final publication is available at Springer via http://dx.doi.org/10.1007/s11227-015-1387-y

Funding: Czech Science Foundation (GACR project 14-31593S), through European Social Fund within the project CZ.1.07/2.3.00/20.0296 and SP2014/111, an SGS project of Faculty of Economics, VŠB-Technical University of Ostrava.

Keywords

  • artificial neural network
  • data envelopment analysis (DEA)
  • Levenberg–Marquardt (LM)
  • LM-DEA
  • negative data
  • SORM-DEA

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