Data envelopment analysis (DEA) calculates the relative efficiency of homogenous decision-making units (DMUs) with multiple inputs and outputs. Classic DEA models usually suffer from several issues such as: discrimination power, variable weights of inputs/outputs, inaccurate efficiency estimation for small number of DMUs, incapability in working with zero and negative data, and not having exter...

Super-efficiency model in the presence of negative data is a relatively neglected issue in the DEA field. The existing super-efficiency models have some shortcomings in practice. In this paper, a novel VRS radial super-efficiency DEA model based on Directional Distance Function (DDF) is proposed to provide a complete ranking order of units (including efficient and inefficient ones). The propose...

This paper presents a method for performance evaluation, ranking and clustering based on the double-frontier view to analyze the complex networks. The model allows us to open the structure of the “black box” and can help to obtain important information about efficient and inefficient points of the system. In this paper, we consider a three-stage network, in respect to the additional desirable a...

in this paper, we consider the production possibility set with n production units such that the following four principles that governs: inclusion observations, conceivability, immensity and convexity. our goal is to estimate the output of a same and new production unit with existing production possibility and amount of input is specified. so, initially we find the interval changes of each input...

The ability of determining all defining hyperplanes of DEA production possibility set (efficient frontier) prior to the DEA computations is of extreme importance. Specially, access to efficient frontier permits a complete analysis (e.g. calculation of efficiency scores, returns to scale, sensitivity analysis and so on) in second phase for the corresponding model. This paper presents a linear sy...

Basic models of Data Envelopment Analysis are intrinsically preference-free, in the sense that they consider all inputs and outputs and also all decision making units of the same importance. Although this property is beneficial in many ways, it has some drawbacks simultaneously, as the decision makers’ preferences are not taken into account in the process of evaluating units.  To overcome this ...

Data Envelopment Analysis (DEA) is a mathematic technique to evaluate the relative efficiency of a group of homogeneous decision making units (DMUs) with multiple inputs and outputs. The efficiency of each unit is measured based on its distance to the production possibility set (PPS). In this paper, the BCC model is used in output-oriented. The average return on profit as output and the covaria...

Anchor points play an important role in DEA theory and application. They define the transition from the efficient frontier to the “free-disposability” portion of the boundary. Our objective is to use the geometrical properties of anchor points to design and test an algorithm for their identification. We focus on the variable returns to scale production possibility set; our results do not depend...

Data Envelopment Analysis (DEA) is the methodology for evaluating the relative productive efficiency of decision making units (DMUs) that produce multiple-outputs using multiple-inputs. DEA was proposed for the first time in 1957 by Farrell; nonetheless, the wide usage of this method begun with its generalization and the linear programming formulation which is due to Charnes et al. (1978) (for ...