A New Approach to Measuring Congestion in DEA with Common Weights

Measuring congestion in data envelopment analysis with common weights

measuring congestion in data envelopment analysis with common weights

Deriving Common Set of Weights in the Presence of the Undesirable Inputs: A DEA based Approach

Data Envelopment Analysis (DEA) as a non-parametric method for efficiency measurement allows decision making units (DMUs) to select the most advantageous weight factors in order to maximize their efficiency scores.  In most practical applications of DEA presented in the literature, the presented models assume that all inputs are fully desirable. However, in many real situations undesirable inpu...

measuring efficiency using fuzzy dea and fuzzy constraints to control weights and to find a common set of weights

in this paper it has been tried to present a model by usingsome changes in the classical dea models and combining these modelswith fuzzy models to eliminate these weaknesses. inputs and outputs arerecognized by using mcdm and ahp techniques and experts views. inthe next step, the gathered data is compounded by fuzzy techniques andentered in the special dea model after changing to the crisp amou...

Fuzzy Efficiency Measures in DEA: A New Approach based on Fuzzy DEA Approach with Double Frontiers

Data envelopment analysis (DEA) is a method to measure relative efficiency of a set of decision-making units (DMUs) which uses multiple inputs and produces multiple outputs. In the conventional DEA, crisp inputs and outputs are fundamentally necessary. But the observed values of inputs and outputs in real-world problems are sometimes imprecise. Thus, performance measurement often needs to be do...