A non-radial directional distance method on classifying inputs and outputs in DEA: Application to banking industry

Mehdi Toloo*, Maryam Allahyar, Jana Hančlová

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

28 Citations (Scopus)


The original Data Envelopment Analysis (DEA) models have required an assumption that the status of all inputs and outputs be known exactly, whilst we may face a case with some flexible performance measures whose status is unknown. Some classifier approaches have been proposed in order to deal with flexible measures. This contribution develops a new classifier non-radial directional distance method with the aim of taking into account input contraction and output expansion, simultaneously, in the presence of flexible measures. To make the most appropriate decision for flexible measures, we suggest two pessimistic and optimistic approaches from both individual and summative points of view. Finally, a numerical real example in the banking system in the countries of the Visegrad Four (i.e. Czech Republic, Hungary, Poland, and Slovakia) is presented to elaborate applicability of the proposed method.

Original languageEnglish
Pages (from-to)495-506
Number of pages12
JournalExpert Systems with Applications
Publication statusPublished - Feb 2018
Externally publishedYes


  • Data envelopment analysis
  • Directional distance function
  • Flexible measure
  • Mixed integer linear programming
  • Non-radial non-oriented models

ASJC Scopus subject areas

  • Engineering(all)
  • Computer Science Applications
  • Artificial Intelligence

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