Data envelopment analysis (DEA) is a methodology for measuring the relative efficiencies of a set of decision-making units (DMUs) that use multiple inputs to produce multiple outputs. The standard DEA models assume that all inputs and outputs are crisp and can be changed at the discretion of management. While crisp input and output data are fundamentally indispensable in the standard DEA evalua...

Data Envelopment Analysis (DEA) provides means for piecewise linear approximation of production functions. To improve the fit to the data we propose to improve the flexibility of the frontier by extending DEA towards a more general piecewise quadratic approximation, called Quadratic Data Envelopment Analysis (QDEA). In contrast to the linear approximation, the quadratic approximation allows for...

It is generally accepted that Data Envelopment Analysis (DEA) is a method for indicating efficiency. The DEA method has many applications in the field of calculating the relative efficiency of Decision Making Units (DMU) in explicit input-output environments. Regarding imprecise data, several definitions of efficiency can be found. The aim of our work is showing an equivalence relation between ...

  In this paper, different input-oriented ratio-based DEA (DEA-R-I) models are proposed. By presenting the envelopment-additive-enhanced Russell model based on the DEA-R-I form, each decision making unit is evaluated and its efficiency score is calculated. Also, by presenting the central resource allocation model based on the DEA-R-I form, a suitable benchmark for all DMUs is proposed by solvin...

Cost efficiency measures the cost of resource by output production. While conventional cost efficiency models set targets separately for each DMU, There are cases where the Central decision making is seeking the above targets, and at the same tries to obtain the target of Min cost efficiency for the total consumption. in this paper we consider that there is a centralized decision maker (DM). In...

The statistical properties of the efficiency estimators based on Data Envelopment Analysis (DEA) are largely unknown. Recent work by Simar et al. and Banker has shown the consistency of the DEA estimators under specific assumptions, and Banker proposes asymptotic tests of whether two subsamples have the same efficiency distribution. There are difficulties arising from bias in small samples and ...

Although data envelopment analysis (DEA) has been extensively used to assess the performance of mutual funds (MF), most of the approaches overestimate the risk associated to the endogenous benchmark portfolio. This is because in the conventional DEA technology the risk of the target portfolio is computed as a linear combination of the risk of the assessed MF. This neglects the important effects...

In many real applications, the data of production processes can't be precisely measured.We develop some fuzzy versions of the classical DEA models (in particular, the CCRmodel) by using some ranking methods based on the comparison of cuts. Our approachescan be seen as an extension of the DEA methodology. The provides users and practitionerswith models which represent some real life processes mo...

the problem of utilizing undesirable (bad) outputs in dea models often need replacing the assumption of free disposability of outputs by weak disposability of outputs. the kuosmanen technology is the only correct representation of the fully convex technology exhibiting weak disposability of bad and good outputs. also, there are some specific features of non-radial data envelopment analysis (dea...

In original data envelopment analysis (DEA) models, the data for all inputs and outputs are known exactly. When some inputs and outputs are unknown decision variables, such as interval data, ordinal data, and ratio bounded data, the DEA model is called imprecise DEA (IDEA). In this paper, We develop an alternative approach based upon slacksbased measure of efficiency (SBM) for dealing with inte...