A Robust Two-stage Data Envelopment Analysis Model for Measuring Efficiency: Considering Iranian Electricity Power Production and Distribution Processes

This paper presents a new robust two-stage Data Envelopment Analysis (DEA) for efficiency evaluation of the electricity power production and distribution companies. DEA has been widely used for benchmarking the electricity companies. Traditional studies in DEA consider systems as a whole when measuring the efficiency, ignoring the operation of individual processes within a system. To tackle this issue, many works, aptly labeled Network DEA (NDEA), have been done to decompose the decision making units (DMU) overall efficiency. The two-stage DEA model is a special variation which evaluates the efficiency of the DMUs having a two-stage internal structure where the initial inputs are transformed to the intermediate outputs and intermediate outputs are developed into final output in the second stage. Conventional two stage data envelopment analysis (DEA) models require the exact data of inputs or outputs. However, in many real world applications this simple assumption does not hold. Recently, the robust optimization technique has been introduced for entering perturbation in the mathematical programming problem such as two-stage DEA. This paper adopts this concept with the existing two-stage DEA model. The implementation of the proposed method of this paper is applied for ranking different electricity power production and distribution companies in Iran