ScholarLens Back to search
2013•International Journal of Operational ResearchRequires access

An application of multi-component ranking in banks by context-dependent DEA for non-extreme efficient DMUs

Farhad Hosseinzadeh Lotfı, G.R. Jahanshahloo, Mohsen Vaez-Ghasemi, Zohreh Moghaddas

Open publisher page 1 citations

Abstract

In most applications of data envelopment analysis (DEA) presented in the literature, the models are designed to acquire a single measure of efficiency. However in many instances, the decision making units (DMUs) can be separated into different components. This paper deals with ranking in DEA analysis of multi-component decision making units (DMUs). Using DEA technology, we first show how efficiency of each unit and its components can be obtained then from a practical viewpoint the issue of ranking multicomponent decision making units will be discussed. The significant feature of this method is that it does not have much to do with theoretical concepts and it is has been mostly taught over for applicational issues.

About this research paper

What this paper is about

In most applications of data envelopment analysis (DEA) presented in the literature, the models are designed to acquire a single measure of efficiency. However in many instances, the decision making units (DMUs) can be separated into different components. This paper deals with ranking in DEA analysis of multi-component decision making units (DMUs). Using DEA technology, we first show how efficiency of each unit and its components can be obtained then from a practical viewpoint the issue of ranking multicomponent decision making units will be discussed. The significant feature of this method is that it does not have much to do with theoretical concepts and it is has been mostly taught over for applicational issues.

Why it matters

OpenAlex reports 1 citations for this work. Citation counts describe recorded attention and do not establish research quality.

Key contribution

A contribution statement is not available in the OpenAlex record.

Method / approach

Method details are not available in the OpenAlex metadata.

Main findings

Findings are not separately available in the OpenAlex metadata.

Limitations

Limitations are not available in the OpenAlex metadata.

Applications

Application details are not available in the OpenAlex metadata.

Available abstract

In most applications of data envelopment analysis (DEA) presented in the literature, the models are designed to acquire a single measure of efficiency. However in many instances, the decision making units (DMUs) can be separated into different components. This paper deals with ranking in DEA analysis of multi-component decision making units (DMUs). Using DEA technology, we first show how efficiency of each unit and its components can be obtained then from a practical viewpoint the issue of ranking multicomponent decision making units will be discussed. The significant feature of this method is that it does not have much to do with theoretical concepts and it is has been mostly taught over for applicational issues.

Key concepts: Data envelopment analysis, Ranking (information retrieval), Computer science, Component (thermodynamics), Context (archaeology), Measure (data warehouse), Feature (linguistics), Data mining

Related papers

Related paper 1Related paper 2Related paper 3
Back to paper searchBrowse research topicsOriginal source
An application of multi-component ranking in banks by context-dependent DEA for non-extreme efficient DMUs — Research Paper | ScholarLens