A new robust DEA method to recognize the anchor points in the presence of uncertain data

Abstract

One of the most well-known issues in Data Envelopment Analysis (DEA) literature is to identify the anchor points of the production possibility set (PPS). Each extreme efficient unit which is located on the intersection of the strong and weak efficient frontiers of the PPS, is called an anchor point. In the other word, a decision making unit (DMU) is an anchor point, if there is at least one supporting hyperplane at the unit under consideration, in the situation that some components of its gradient vector are equal to zero, and so some input or output factors do not play any role in the performance of that unit. This study presents a new method to identify the anchor points of the PPS under the variable returns to scale (VRS) assumption and in the presence of the uncertain data. The proposed method is based on the robust optimization technique and finding the weak and strong defining supporting hyperplanes passing through the unit under evaluation. The potentially of the proposed method is illustrated by a data set, includes 20 banks in Iran.