Performance measure selection problem with both orientations of envelopment forms in DEA

Esmaeil Keshavarz, Mehdi Toloo*

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review


Performance evaluation of some independent and homogeneous organizations, based on relative efficiency, has economic production theory as its foundation. Data envelopment analysis (DEA) is a nonparametric approach to evaluate the performance of some homogeneous decision making units (DMUs) with multiple inputs and multiple outputs. If the number of performance measures is high in comparison with the number of DMUs, then a large percentage of DMUs will be identified as efficient, which practically is not favorable. We extend a new approach which combines both input-and output-oriented envelopment DEA models to select a subset of performance measures under variable returns to scale assumption. In this approach, a mixed binary linear programming (MBLP) model is formulated which employs the rough rule of thumb in DEA to select the most influential performance measures. Practically, we utilize a real dataset as an example to illustrate the potential application of the proposed approach.

Original languageEnglish
JournalProceedings of International Conference on Computers and Industrial Engineering, CIE
Publication statusPublished - 2018
Externally publishedYes
Event48th International Conference on Computers and Industrial Engineering, CIE 2018 - Auckland, New Zealand
Duration: Dec 2 2018Dec 5 2018


  • Data envelopment analysis (DEA)
  • Envelopment form
  • Input-and output-oriented models
  • Performance evaluation
  • Selective measures

ASJC Scopus subject areas

  • Computer Science(all)
  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • Industrial and Manufacturing Engineering
  • Safety, Risk, Reliability and Quality

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