A selecting model under constant returns to scale in data envelopment analysis

Mehdi Toloo, Maryam Allahyar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Data envelopment analysis (DEA) is a data-oriented mathematical programming approach that evaluates a set of peer decision making units (DMUs) dealing directly with the observed inputs and outputs (performance measures). Empirically, in order to have a logical assessment, there should be a balance between the number of performance measures and the number of DMUs. Accordingly, applying an appropriate method so that one can select some performance measures is very crucial for successful applications. In this paper, we suggest the envelopment form of selecting model under constant returns to scale (CRS) from both individual and aggregate points of view. We also show that applying these selecting models leads to the maximum discrimination between efficient units.

Original languageEnglish
Title of host publicationSMSIS 2017 - Proceedings of the 12th International Conference on Strategic Management and its Support by Information Systems 2017
EditorsRadek Nemec, Lucie Chytilova
PublisherVSB-Technical University of Ostrava
Pages350-357
Number of pages8
ISBN (Electronic)9788024840468
Publication statusPublished - 2017
Externally publishedYes
Event12th International Conference on Strategic Management and its Support by Information Systems 2017, SMSIS 2017 - Ostrava, Czech Republic
Duration: May 25 2017May 26 2017

Publication series

NameSMSIS 2017 - Proceedings of the 12th International Conference on Strategic Management and its Support by Information Systems 2017

Conference

Conference12th International Conference on Strategic Management and its Support by Information Systems 2017, SMSIS 2017
Country/TerritoryCzech Republic
CityOstrava
Period5/25/175/26/17

Keywords

  • Data envelopment analysis
  • Efficiency
  • Selective measures
  • The rule of thumb

ASJC Scopus subject areas

  • Information Systems and Management
  • Management Science and Operations Research
  • Management Information Systems
  • Information Systems
  • Strategy and Management

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