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...

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...

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...

In the today’s world wherein every routine is based on economic factors, there is no doubt that theoretical sciences are driven by their capabilities and affordances in terms of economy. As a mathematical tool, data envelopment analysis (DEA) is provided to economics, so that one can investigate associated costs, prices and revenues of economic units. Data Envelopment Analysis (DEA) is a linear...

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...

Data Envelopment Analysis (DEA) is a great approach used for measuring relative efficiencies and performance of a collection of Decision Making Units (DMUs). These used in the various forms, such as hospitals, universities, air force, banks, courts, business firms, and others, including the performance of countries, regions, etc. One of the earliest applications of DEA called Education. It was ...

More than 200 papers have been published in the last 20 years on the topic of health supply chains (HSC). Looking at the research methodologies employed, less than 15 papers apply data envelopment analysis (DEA) models. This is in contrast to, for example, A Network Data Envelopment Analysis (NDEA) Model for Supply Chain Performance Evaluation where several reviews on respective NDEA models hav...

This research presents a new application of Data Envelopment Analysis (DEA) to empirically determine prices of multiple products. The DEA technique is now widely applied to performance analysis and efficiency measurement in economics, business and other areas in social science. This study does not explore such conventional DEA uses, rather directing towards a new DEA application to the determin...

Data envelopment analysis (DEA) is a non-parametric approach to evaluate a set of decision making units (DMUs) consuming multiple inputs to produce multiple outputs. Formally, DEA use to estimate the efficiency score into the empirical efficient frontier. Also, DEA can be used to allocate resources and set targets for future forecast. The data are continuous in the standard DEA model whereas th...

Utilizing recent developments in data envelopment analysis (DEA), this paper examines the performance of the top 55 U.S. commercial banks via a two-stage production process that separates profitability and marketability. Substantial performance inefficiency is uncovered in both dimensions. Relatively large banks exhibit better performance on profitability, whereas smaller banks tend to perform ...