Sensitivity Analysis with Fuzzy Data in DEA

DEA (Deta Envelopment Analysis) is a non-parametric technique for measuring the efficiency of DMUs (Decision Making Units)with common inputs and outputs [2,5]. viewpoint for each DMU because of taking a maximum ratio. During recent years, the issue of sensitivity and stability of data envelopment analysis results has been extensively studied. The first DEA sensitivity analysis paper by Charnes et al. [3] examined change in a single output. This was followed by a series of sensitivity analysis articles by Charnes and Neralic [4]in which sufficient conditions for preserving efficiency are determined. Another type of DEA sensitivity analysis is based on super-efficiency DEA approach in which the DMU under evaluation is not included in the reference set [1,11]. Charnes et al. [6,7] developed a super-efficiency DEA sensitivity analysis technique for the situation where simultaneous proportional change is assumed in all inputs and outputs for a specific DMU under consideration. In traditional DEA models, it is assumed that all inputs and outputs are exactly known. But in real world, this assumption may not always be true. On the other hand, in more general cases, the data for evaluation are stated by natural language, such as good or bad, to reflect the general situation. So some researchers have propose DEA fuzzy models to evaluate DMUs with fuzzy data [9,10,12]. However, methods of sensitivity analysis proposed in DEA are not suitable