this paper processes a combined method, based on vikor and data envelopment analysis (dea) to select the units with most efficiency. we utilize the vikor as compromise solution method. this research is a two-stage model designed to fully rank the alternatives, where each alternative has multiple inputs and outputs. the problem involves belief parameters in the solution procedure. first, the alt...

The fundamental weakness of a Data Envelopment Analysis (DEA) is its weak discrimination in cases when small number of Decision Making Units (DMU) have been compared. Therefore in such cases the basic DEA model is used in combination with other methods or additional constraints are added to the model. In this paper, the Cross-efficiency method has been combined with the Potential Method (PM).

This paper is aimed to give an illustration on the application of Data Envelopment Analysis (DEA) as a tool to assess Quality Management (QM) efficiency. A variant of DEA, slack based measure (SBM) is used for this purpose. From this study, it is found that DEA is suitable to measure QM efficiency and give improvement suggestions to the inefficient QM. Keywords—Quality Management, Data Envelopm...

This paper proposes the macro model and micro model for the efficiency evaluation of data warehouses (DWH), and introduces Fuzzy Data Envelopment Analysis (FDEA) and Data Envelopment Analysis (DEA) to comprehensively assess DWH efficiency. FDEA can determine the efficiencies of data warehouses with multiple input / output variables, and also resolve the problem that standard DEA cannot handle w...

In this paper, we re-examine Data Envelopment Analysis models from perspectives of preference order, production set and performance measure. we investigate the relationship between Data Envelopment Analysis (DEA) and Multiple Criteria Decision Making Theory. There are three key building blocks in a DEA model: preference order, production possibility set and performance measure. It is shown in t...

Data envelopment analysis (DEA) is a mathematical programming technique for identifying efficiency scores of decision making units (DMUs). Since DEA models cannot present efficient frontiers of PPS, in order to do this, we introduce a method for identifying efficient frontier for DMUs with interval data.

Problem statement: A variety of methods and algorithms for classification problems have been developed recently. But the main question is that how to select an appropriate and effective classification algorithm. This has always been an important and difficult issue. Approach: Since the classification algorithm selection task needs to examine more than one criterion such as accuracy and computat...

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. Crisp input and output data are fundamentally indispensable in conventional DEA. However, the observed values of the input and output data in real-world problems are sometimes imprecise or vague. Many researchers ...

Our motivation is to detail a potential improvement on the three-stage analysis published by Fried et al. [Accounting for environmental effects and statistical noise in data envelopment analysis. Journal of Productivity Analysis 2002;17:157–74] that can distinguish true performers from those that may be advantaged by favourable environments or measurement errors. The method starts with data env...

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...