Alternative solutions for classifying inputs and outputs in data envelopment analysis

Mehdi Toloo*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

39 Citations (Scopus)

Abstract

In conventional data envelopment analysis (DEA) models, a performance measure whether as an input or output usually has to be known. Nevertheless, in some cases, the type of a performance measure is not clear and some models are introduced to accommodate such flexible measures. In this paper, it is shown that alternative optimal solutions of these models has to be considered to deal with the flexible measures, otherwise incorrect results might occur. Practically, the efficiency scores of a DMU could be equal when the flexible measure is considered either as input or output. These cases are introduced and referred as share cases in this study specifically. It is duplicated that share cases must not be taken into account for classifying inputs and outputs. A new mixed integer linear programming (MILP) model is proposed to overcome the problem of not considering the alternative optimal solutions of classifier models. Finally, the applicability of the proposed model is illustrated by a real data set.

Original languageEnglish
Pages (from-to)1104-1110
Number of pages7
JournalComputers and Mathematics with Applications
Volume63
Issue number6
DOIs
Publication statusPublished - Mar 2012
Externally publishedYes

Keywords

  • Data envelopment analysis
  • Efficiency
  • Flexible measure
  • Mixed integer linear program

ASJC Scopus subject areas

  • Modelling and Simulation
  • Computational Theory and Mathematics
  • Computational Mathematics

Cite this