Data Envelopment Analysis (DEA) is a relatively new “data oriented” non-parametric approach for evaluating the performance of complex entities called Decision Making Units (DMUs) which convert multiple inputs into multiple outputs. DEA as a linear programming procedure computes a comparative ratio of outputs to inputs for each DMU, which is reported as the relative efficiency score. In a relati...

In this paper, a data envelopment analysis (DEA) model with weight constraints is proposed to evaluate the work safety supervision in 18 districts. Conventional evaluation approaches of the work safety supervision only consider the simple data such as the number of accidents and the number of death in the accidents. There are two major shortcomings: the weights of criteria are difficult to dete...

banks are one of the most important financial sectors in order to the economic development of each country. certainly, efficiency scores and ranks of banks are significant and effective aspects towards future planning. sometimes the performance of banks must be measured in the presence of undesirable and vague factors. for these reasons in the current paper a procedure based on data envelopment...

(2011). " Formative evaluation of electricity distribution utilities using Data Envelopment Analysis ". Abstract — The use of Data Envelopment Analysis (DEA) in the electricity distribution sector has been prolific in the number of papers published in research journals. However, while numerous studies have been documented, they have mostly been summative. Their aim has been predominantly descri...

Data Envelopment Analysis (DEA) has been applied in many efficiency studies in the banking sector. Conventional DEA models consider the system as a single-process black box. There are however a number of so-called network DEA approach that consider the system as composed by distinct processes or stages, each one with its own inputs and outputs and with intermediate flows among the stages. In th...

This article compares two techniques: Data Envelopment Analysis (DEA) and Factor Analysis (FA) to aggregate multiple inputs and outputs in the evaluation of decision making units (DMU). Dِata envelopment analysis (DEA), a popular linear programming technique, is useful to rate comparatively operational efficiency of DMUs based on their deterministic or stochastic input–output data. Factor analys...

this paper presents a combination of data envelopment analysis (dea) and principal component analysis (pca) to reduce the dimensionality of data set. dea is known as effective tool for assessment and benchmarking. the weak point of dea, it is that the number of efficient dmus relies on the number of variables (inputs and outputs). for solving this, first, we do principal component analysis (pca...

The existing data envelopment analysis (DEA) models for measuring the relative efficiencies of a set of decision making units (DMUs) using various inputs to produce various outputs are limited to crisp data. The notion of fuzziness has been introduced to deal with imprecise data. Fuzzy DEA models are made more powerful for applications. This paper develops the measure of efficiencies in input o...

This paper is drawn from the use of Data Envelopment Analysis (DEA) in helping a Portuguese bank to manage the performance of its branches. The bank wanted to set targets for the branches on such variables as growth in number of clients, growth in funds deposited and so on. Such variables can take positive and negative values but traditional DEA models, with some specialised exceptions, have hi...

Data Envelopment Analysis (DEA) is one of the most widely used methods in the measurement efficiency and productivity of Decision Making Units (DMUs). DEA for a large dataset with many inputs/outputs would require huge computer resources in terms of memory and CPU time. This paper introduces a neural network backpropagation Data Envelopment Analysis. Neural network requirements of computer memo...