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) is a useful tool based on which inefficient decision-making units (DMUs) can perform a benchmarking process to improve their performance. However, several practical problems need to be addressed in benchmark target selection. One issue discussed in this research is that it might not be feasible for an inefficient DMU to achieve its target’s efficiency in a single...

Abstract The main resources needed for human survival are water, energy and food, the research on relationship among three has become topic. Due to shortage of natural resources, efficiency water–energy–food (WEF) nexus a hot issue scholars governments all over world. simple models cannot study correlation them; methods WEF gradually shift from single integrated model. data envelopment analysis...

Data Envelopment Analysis (DEA) is one of the best methods for measuring the efficiency and productivity of Decision Making Units (DMU). Evaluating the efficiency of DMUs which have two or several stages by using the conventional DEA models, is equal to consider them as black box. This method, omits the effect of intermediate measure on efficiency. Therefore, just the first network inputs and t...

Data envelopment analysis (DEA), as originally proposed, is a methodology for evaluating the relative efficiencies of a set of homogeneous decision-making units (DMUs) in the sense that each uses the same input and output measures (in varying amounts from one DMU to another). In some situations, however, the assumption of homogeneity among DMUs may not apply. As an example, consider the case wh...

We provide an alternative framework for solving Data Envelopment Analysis (DEA) models which, in comparison with the standard Linear Programming (LP) based approach that solves one LP for each Decision Making Unit (DMU), delivers much more information. By projecting out all the variables which are common to all LP runs, we obtain a formula into which we can substitute the inputs and outputs of ...

The motivation of this paper is to propose such equitable method for ranking all decision making units (DMUs) in dynamic Data Envelopment Analysis (DDEA) framework. As far as we are aware there is not more studies in dynamic DEA literature. What's more, in such cases the best operating unit is important to be sampled for the others in under evaluated time periods. However, in this special conce...

Data envelopment analysis is a new field of interdisciplinary research in operations management science and mathematical economics. uses programming to evaluate the relationship between decision-making units with multiple inputs outputs (abbreviated as DMU). Effectiveness (DEA effective) judge whether DMU located on "frontier surface" production possibility set. This paper proposes method for e...

Stepwise benchmark target selection in data envelopment analysis (DEA) is a realistic and effective method by which inefficient decision-making units (DMUs) can choose benchmarks in a stepwise manner. We propose, for the construction of a benchmarking network (i.e., a network structure consisting of an alternative sequence of benchmark targets), an approach that integrates the cross-efficiency ...

Data envelopment analysis (DEA) is a non-parametric method for evaluating the relative efficiency of decision making units (DMUs) on the basis of multiple inputs and outputs. The context-dependent DEA is introduced to measure the relative attractiveness of a particular DMU when compared to others. In real-world situation, because of incomplete or non-obtainable information, the data (Input and ...