Revenue congestion: An application of data envelopment analysis

Window Network Data Envelopment Analysis: An Application to Investment Companies

In this study, the window network data envelopment analysis (WNDEA) model will be proposed, that is capable to be used in the presence of panel data. Additionally, the proposed model is applied to evaluate the dynamic efficiency of 5 investment companies in Tehran stock exchange during the period from 2013 to 2017.

Improving envelopment in data envelopment analysis by means of unobserved DMUs: an application of banking industry

In data envelopment analysis, the relative efficiency of a decision making unit (DMU) is defined as the ratio of the sum of its weighted outputs to the sum of its weighted inputs allowing the DMUs to freely allocate weights to their inputs/outputs. However, this measure may not reflect a the true efficiency of a DMU because some of its inputs/outputs may not contribute reasonably in computing t...

Measuring congestion in data envelopment analysis with common weights

An Approach to Identify and Evaluate Congestion in Data Envelopment Analysis

Congestion indicates an economic state where inputs are overly invested. Evidence of congestion occurs whenever reducing some inputs can increase outputs. In this paper, we present a new model to identify and evaluate congestion in Data Envelopment Analysis (DEA). We use output efficient DMUs to construct our proposed model to evaluate congestion. We also proposed a linear inequality and equali...

An Extension to Imprecise Data Envelopment Analysis

The standard data envelopment analysis (DEA) method assumes that the values for inputs and outputs are exact. While DEA assumes exact data, the existing imprecise DEA (IDEA) assumes that the values for some inputs and outputs are only known to lie within bounded intervals, and other data are known only up to an order. In many real applications of DEA, there are cases in which some of the input ...