| Conventional data envelopment analysis (DEA) requires deterministic input/output data for efficiency evaluation of a set of decision making units (DMUs). In many applications, however, uncertainty is associated with some of the input and output data; consequently, the results of super efficiency are stochastic as well. In this paper, we introduce the notion of stochastically super-efficient DMUs using the super efficiency distribution of DMUs. We then propose some ranking methods for performance evaluation in stochastic DEA based on the super efficiency distribution of DMUs. We demonstrate how the proposed ranking methods can be implemented using a real data set. |
