An Approach to Identify and Evaluate Congestion in Data Envelopment Analysis

An improved approach to find and rank BCC-efficient DMUs in data envelopment analysis (DEA)

Recently, a mixed integer data envelopment analysis (DEA) model has been proposed to find the most BCC-efficient (or the best) decision making unit (DMU) by Toloo (2012). This paper shows that the model may be infeasible in some cases, and when the model is feasible, it may fail to identify the most efficient DMU, correctly. We develop an improved model to find the most BCC-efficient DMU that r...

Measuring congestion in data envelopment analysis with common weights

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 ...

An Alternative Secondary Goal Approach to Modify Cross Efficiency Evaluation in Data Envelopment Analysis

The cross efficiency evaluation is used to performance measurement of decision making units in data envelopment analysis concept. One of the most important shortcoming of this method is existing alternative optimal solution and therefore, the efficiency scores are not unique. We are going to summarize the pervious models proposed by researchers and suggest an alternative secondary goal approach...

Computation of Output Losses due to Congestion in Data Envelopment Analysis

     Data Envelopment Analysis (DEA) is an approach for evaluating performances of Decision Making Units (DMUs). The performances of DMUs are affected by the amount of sources that DMUs used. Usually increases in inputs cause increases in outputs. However, there are situations where increases in one or more inputs generate a reduction in one or more outputs. In such situations there is congesti...

A Recurrent Neural Network to Identify Efficient Decision Making Units in Data Envelopment Analysis

In this paper we present a recurrent neural network model to recognize efficient Decision Making Units(DMUs) in Data Envelopment Analysis(DEA). The proposed neural network model is derived from an unconstrained minimization problem. In theoretical aspect, it is shown that the proposed neural network is stable in the sense of lyapunov and globally convergent. The proposed model has a single-laye...