Data envelopment analysis (DEA) models have been used in formulating the Malmquist index to measure productivity change over time periods. In this article, we question the validity of this index in the presence of input/output slacks that appear in DEA models used. We demonstrate with an application to banking that Malmquist index loses its meaning whenever slacks are present. As a corrective m...

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...

Literature of productive efficiency analysis is currently divided between two main paradigms: the parametric Stochastic Frontier Analysis (SFA) and the deterministic, nonparametric Data Envelopment Analysis (DEA). This paper develops a new encompassing framework that melds the SFA-style stochastic composite error term to the DEA-type nonparametric frontier that satisfies monotonicity and concav...

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...

data envelopment analysis (dea) is a mathematical programming method in operations research that can be used to distinguish between efficient and inefficient decision making units (dmus). however, the conventional dea models do not have the ability to rank the efficient dmus. this article suggests bootstrapping method for ranking measures of technical efficiency as calculated via non-radial mod...

 Recently, Khodabakhshi and Aryavash have introduced a ranking method [Applied Mathematics Letters, 25 (2012) 2066-2070.], which is based on an optimistic-pessimistic approach of data envelopment analysis (DEA). This method ranks all decision making units according to a combination of their minimum and maximum possible efficiency scores which are determined by solving two linear programming mo...

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...

This paper compares multi-criteria decision aiding (MCDA) and data envelopment analysis (DEA) approaches for assessing renewable energy plants, in order to determine their performance in terms of economic, environmental, and social criteria and indicators. The case is for a dataset of 41 agricultural biogas plants in Austria using anaerobic digestion. The results indicate that MCDA constitutes ...