In this current study a generalized super-efficiency model is first proposed for ranking extreme efficient decision making units (DMUs) in stochastic data envelopment analysis (DEA) and then, a deterministic (crisp) equivalent form of the stochastic generalized super-efficiency model is presented. It is shown that this deterministic model can be converted to a quadratic programming model. So far several approaches have been proposed in classic DEA by many researchers for ranking of efficient decision making units. In the previous proposed approaches, all inputs and outputs are respectively considered as deterministic (crisp) inputs and outputs while in real world, stochastic data may be present. It is necessary to mention that advantage of our proposed approach is capable of ranking of efficient decision making units with stochastic inputs and outputs. At last, an illustrative example highlights the proposed model and also a concluding comment, future extensions, and suggest possible future direction of research are all summarized.