in this research, interval efficiency of butchery units of zabol city using model of interval data envelopment analysis was measured, and factors of effective on it was studied. data for this research obtained via completing questionnaires and use of method of sampling randomized for 2010 years. the results showed that efficiency maximum of 50% of butcheries is less than 40% and efficiency mini...

data envelopment analysis (dea) is a mathematical programming-based approach for evaluates the relative efficiency of a set of dmus (decision making units). the relative efficiency of a dmu is the result of comparing the inputs and outputs of the dmu and those of other dmus in the pps (production possibility set). also, in data envelopment analysis various models have been developed in order to...

Data envelopment analysis (DEA) is an approach to measure the relative efficiency of decision-making units with multiple inputs and multiple outputs using mathematical programming. In the traditional DEA, it is assumed that we know the input or output role of each performance measure. But in some situations, the type of performance measure is unknown. These performance measures are called flexi...

Recently the concept of facility efficiency, which defined by data envelopment analysis (DEA), introduced as a location modeling objective, that provides facilities location’s effect on their performance in serving demands. By combining the DEA models with the location problem, two types of “efficiencies” are optimized: spatial efficiency which measured by finding the least cost location and al...

Data envelopment analysis (DEA) is a non-parametric technique for evaluation of relative efficiency of decision making units described by multiple inputs and outputs. It is based on solving linear programming problems. Since 1978 when basic DEA model was introduced many its modifications were formulated. Among them are two or multi-stage models with serial or parallel structure often called net...

The problem of the sample variance computation for epistemic inter-val-valued data is, in general, NP-hard. Therefore, known efficient algorithms for computing variance require strong restrictions on admissible intervals like the no-subset property or heavy limitations on the number of possible intersections between intervals. A new asymptotic algorithm for computing the upper bound of the samp...

This paper investigates the identification of quantiles and quantile regression parameters when observations are set valued. We define random sets in a way that extends definition for regular variables. then give sharp characterization this by extending concepts from theory. Applying its sharpness to parametric models yields sharpness. apply our methods data on localized environmental benefits ...