Selective measures in data envelopment analysis

Mehdi Toloo*, Mona Barat, Atefeh Masoumzadeh

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

35 Citations (Scopus)


Data envelopment analysis (DEA) is a data based mathematical approach, which handles large numbers of variables, constraints, and data. Hence, data play an important and critical role in DEA. Given a set of decision making units (DMUs) and identified inputs and outputs (performance measures), DEA evaluates each DMU in comparison with all DMUs. According to some statistical and empirical rules, a balance between the number of DMUs and the number of performance measures should exist. However, in some situations the number of performance measures is relatively large in comparison with the number of DMUs. These cases lead us to choose some inputs and outputs in a way that produces acceptable results. We refer to these selected inputs and outputs as selective measures. This paper presents an approach toward a large number of inputs and outputs. Individual DMU and aggregate models are recommended and expanded separately for developing the idea of selective measures. The practical aspect of the new approach is illustrated by two real data set applications.

Original languageEnglish
Pages (from-to)623-642
Number of pages20
JournalAnnals of Operations Research
Issue number1
Publication statusPublished - Mar 2014
Externally publishedYes


  • Data envelopment analysis
  • Decision making unit
  • Efficiency
  • Selective measures

ASJC Scopus subject areas

  • General Decision Sciences
  • Management Science and Operations Research

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