Abstract
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 language | English |
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Pages (from-to) | 495-506 |
Number of pages | 12 |
Journal | Expert Systems with Applications |
Volume | 92 |
DOIs | |
Publication status | Published - Feb 2018 |
Externally published | Yes |
Keywords
- Data envelopment analysis
- Directional distance function
- Flexible measure
- Mixed integer linear programming
- Non-radial non-oriented models
ASJC Scopus subject areas
- General Engineering
- Computer Science Applications
- Artificial Intelligence