A bi-objective weighted model for improving the discrimination power in MCDEA

M.-R. Ghasemi, Joshua Ignatius, Ali Emrouznejad

Research output: Contribution to journalArticle

Abstract

Lack of discrimination power and poor weight dispersion remain major issues in Data Envelopment Analysis (DEA). Since the initial multiple criteria DEA (MCDEA) model developed in the late 1990s, only goal programming approaches; that is, the GPDEA-CCR and GPDEA-BCC were introduced for solving the said problems in a multi-objective framework. We found GPDEA models to be invalid and demonstrate that our proposed bi-objective multiple criteria DEA (BiO-MCDEA) outperforms the GPDEA models in the aspects of discrimination power and weight dispersion, as well as requiring less computational codes. An application of energy dependency among 25 European Union member countries is further used to describe the efficacy of our approach.
Original languageEnglish
Pages (from-to)640-650
Number of pages11
JournalEuropean Journal of Operational Research
Volume233
Issue number3
Early online date13 Sep 2013
DOIs
Publication statusPublished - 16 Mar 2014

Fingerprint

Data envelopment analysis
Multiple Criteria
Data Envelopment Analysis
Discrimination
Goal Programming
Efficacy
Union
Model
Energy
Demonstrate
Multiple criteria

Keywords

  • multi-criteria data envelopment analysis
  • goal programming
  • discrimination power
  • weight dispersion
  • multi-objective programming
  • energy policy

Cite this

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A bi-objective weighted model for improving the discrimination power in MCDEA. / Ghasemi, M.-R.; Ignatius, Joshua; Emrouznejad, Ali.

In: European Journal of Operational Research, Vol. 233, No. 3, 16.03.2014, p. 640-650.

Research output: Contribution to journalArticle

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