A combined neural network and DEA for measuring efficiency of large scale datasets

Ali Emrouznejad, Estelle Shale

Research output: Contribution to journalArticle

Abstract

Data Envelopment Analysis (DEA) is one of the most widely used methods in the measurement of the efficiency and productivity of Decision Making Units (DMUs). DEA for a large dataset with many inputs/outputs would require huge computer resources in terms of memory and CPU time. This paper proposes a neural network back-propagation Data Envelopment Analysis to address this problem for the very large scale datasets now emerging in practice. Neural network requirements for computer memory and CPU time are far less than that needed by conventional DEA methods and can therefore be a useful tool in measuring the efficiency of large datasets. Finally, the back-propagation DEA algorithm is applied to five large datasets and compared with the results obtained by conventional DEA.
Original languageEnglish
Pages (from-to)249-254
Number of pages6
JournalComputers and Industrial Engineering
Volume56
Issue number1
Early online date31 May 2008
DOIs
Publication statusPublished - Feb 2009

Keywords

  • neural networks
  • data envelopment analysis
  • large datasets
  • back-propagation DEA

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