Stochastic frontier models along the lines of Aigner et al. are widely used to benchmark firms’ performances in terms efficiency. The typically fully parametric, with functional form specifications for as well both noise and inefficiency processes. Studies such Kumbhakar have attempted relax some restrictions parametric models, but so far all approaches limited a univariate response variable. S...

Data envelopment analysis (DEA) is a non-parametric approach for performance analysis of decision making units (DMUs) which uses a set of inputs to produce a set of outputs without the need to consider internal operations of each unit. In recent years, there have been various studies dealt with two-stage production systems, i.e. systems which consume some inputs in their first stage to produce ...

in this current study a generalized super-efficiency model is first proposed for ranking extreme efficient decision making units (dmus) in stochastic data envelopment analysis (dea) and then, a deterministic (crisp) equivalent form of the stochastic generalized super-efficiency model is presented. it is shown that this deterministic model can be converted to a quadratic programming model. so fa...

Undesirable outputs are often produced together with desirable. This joint production of good and bad outputs bring about a difficulty for productivity measurement. Here we introduce a directional distance function and use it as a component in a new productivity index. This index, as an empirical example shows, seems to solve the problem caused by the joint production of good and bad outputs. P...

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 congestion in...

Since data envelopment analysis (DEA) introduced in 1970s, it has been widely applied to measure the efficiency of a wide variety of production and operation systems. Recently DEA has been extended to examine the efficiency of decision making units (DMUs) with two-stage network structures or processes, where the outputs from the first stage are intermediate measures that make up the inputs of t...

The general practice in performance and production efficiency measurement has been to ignore additional products of most transformation processes that can be classified as " undesirable outputs " — which are a subset of the output set. Without the inclusion of these factors, the efficiency evaluation becomes a purely technical measure of the system alone, and does not account for the interactio...