Data envelopment analysis (DEA) is a method for measuring the efficiency of peer decision making units (DMUs) which uses a set of inputs to produce a set of outputs. In some cases, DMUs have a two-stage structure, in which the first stage utilizes inputs to produce outputs used as the inputs of the second stage to produce final outputs. One important issue in two-stage DEA is the sensitivity of...

Data envelopment analysis is a nonparametric technique checking efficiency of DMUs using math programming. In conventional DEA, it has been assumed that the status of each measure is clearly known as either input or output. Kao and Hwang (2008) developed a data envelopment analysis (DEA) approach for measuring efficiency of decision processes which can be divided into two stages. The first stag...

data envelopment analysis (dea) is a mathematical technique to evaluate the performance of firms with multiple inputs and outputs. in conventional dea models, the efficiency scores of decision making units (dmus) with non-negative inputs and outputs are evaluated in a special period of time. however, in the real world there are situations wherein performance of firms must be evaluated in multip...

in some situations the producers desire to maximize total profit of decision making units (dmus) while the inputs and outputs prices of dmus change from one time period to another. in this paper, the researchers develop productivity index when producers are going to maximize total profit when the price of inputs and outputs are known.the proposed method uses all price information about inputs a...

in many applications, ranking of decision making units (dmus) is a problematic technical task procedure to decision makers in data envelopment analysis (dea), especially when there are extremely efficient dmus. in such cases, many dea models may usually get the same efficiency score for different dmus. hence, there is a growing interest in ranking techniques yet. the purpose of this paper is ra...

Data Envelopment Analysis (DEA) has enjoyed a wide range of acceptance by researchers and practitioners alike as an instrument of performance analysis and management since its introduction in 1978. Many formulations and thousands of applications of DEA have been reported in a considerable variety of academic and professional journals all around the world. Almost all of the formulations and appl...

this paper uses integrated data envelopment analysis (dea) models to rank all extreme and non-extreme efficient decision making units (dmus) and then applies integrated dea ranking method as a criterion to modify genetic algorithm (ga) for finding pareto optimal solutions of a multi objective programming (mop) problem. the researchers have used ranking method as a shortcut way to modify ga to d...

Marketing has a tradition in conducting scientific research with cutting-edge techniques developed in management science, such as data envelopment analysis (DEA) (Charnes et al. 1985). Two decades ago, Kamakura, Ratchford, and Agrawal (1988) applied DEA to examine market efficiency and consumer welfare loss. In this review of three new books, my purpose is (1) to provide a background of DEA for...

Since data envelopment analysis (DEA) introduced in 1970s, it has been widely applied to measure the efficiency of a wide variety of production and operation systems. Recently DEA has been extended to examine the efficiency of decision making units (DMUs) with two-stage network structures or processes, where the outputs from the first stage are intermediate measures that make up the inputs of t...

performance evaluation of universities is an important issue between researchers. classic data envelopment analysis (dea) models with deterministic data have been used by many authors to measure efficiency of universities in different countries. however, dea with stochastic data are, rarely used to measure efficiency of universities. in this paper, input oriented model in stochastic data envelo...