A robust super-efficiency data envelopment analysis model for ranking of provincial gas companies in Iran

Conventional super-efficiency data envelopment analysis (DEA) models require the exact information of inputs or outputs. However, in many real world applications this simple assumption does not hold. Stochastic super-efficiency is one of recent methods which could handle uncertainty in data. Stochastic super-efficiency DEA models are normally formulated based on chance constraint programming. The method is used to estimate the efficiency of various decision making units (DMUs). In stochastic chance constraint super-efficiency DEA, the distinction of probability distribution function for input/output data is difficult and also, in several cases, there is not enough data for estimating of distribution function. We present a new method which incorporates the robust counterpart of super-efficiency DEA. The perturbation and uncertainty in data is assumed as ellipsoidal set and the robust super-efficiency DEA model is extended. The implementation of the proposed method of this paper is applied for ranking different gas companies in Iran.

Research highlights
► This paper develops the super-efficiency data envelopment analysis model with uncertain data. For modeling the uncertainty in data is assumed as ellipsoidal set and the robust optimization technique is applied. The robust super-efficiency DEA model (RSDEA) proposed is immunized the model and solution against of changing in data.