Data envelopment analysis operates as a tool for appraising the relative efficiency of a set of homogenous decision making units. This methodology is applied widely in different contexts. Regarding to its logic, DEA allows each DMU to take its optimal weight in comparison with other DMUs while a similar condition is considered for other units. This feature is a bilabial characteri...

One of the difficulties of Data Envelopment Analysis(DEA) is the problem of de_ciency discrimination among efficient Decision Making Units (DMUs) and hence, yielding large number of DMUs as efficient ones. The main purpose of this paper is to overcome this inability. One of the methods for ranking efficient DMUs is minimizing the Coefficient of Variation (CV) for inputs-outputs weights. In this...

Conventional data envelopment analysis (DEA) assists decision makers in distinguishing between efficient and inefficient decisionmaking units (DMUs) in a homogeneous group. However, DEA does not provide more information about the efficient DMUs. This research proposes a methodology to determine one common set of weights for the performance indices of only DEA efficient DMUs. Then, these DMUs ar...

All decision-making units (DMUs) in the private or public sector are provided with a set of inputs of different values by their governing decisionmaker (GDM), and are required to generate a set of outputs. The GDM is able to reallocate the inputs/outputs among the DMUs to estimate the maximum absolute decision making efficiency of the sector. Serial models are presented to manage the interactio...

The traditional data envelopment analysis (DEA) model can evaluate the relative efficiencies of a set of decision making units (DMUs) with ratio scale inputs and outputs, but it cannot handle interval scale data. This study develop an approach to efficiency analysis to deal with both interval-scale data and ratio scale data. This approach introduces a measure of inefficiency and identifies effi...

Data Envelopment Analysis (DEA) is a technique for measuring the efficiency of a set of Decision Making Units (DMUs) with common data, but in general it is not practical. This paper presents a framework where DEA is used to measure overall profit efficiency with fuzzy data. Specifically, it is shown that as the inputs, outputs and price vectors are fuzzy numbers, the DMUs cannot be easily evalu...

This paper incorporates cones on virtual multipliers of inputs and outputs into DEA analysis. Cone DEA models are developed to generalize the dual of the BCC models as well as congestion models. Input-output data and/or numbers of DMUs for BCC models are inadequate to capture many aspects where judgments, expert opinions, and other external information should be taken into analysis. Cone DEA mo...

There exist many different ranking methods for ranking efficient Decision Making Units (DMUs) in Data Envelopment Analysis (DEA). However, since each of these methods considers a certain theory for ranking, they may give different ranks. In practice, choosing a ranking method, the results of which the Decision Maker (DM) would be able to trust is an important issue. In this paper, we consider s...

We develop comparative results for ratio-based efficiency analysis (REA), based on the decision making units’ (DMUs’) relative efficiencies over sets of feasible weights that characterize preferences for input and output variables. Specifically, we determine (i) ranking intervals which indicate the best and worst efficiency rankings that a DMU can attain relative to other DMUs, (ii) dominance r...