Novel models for obtaining the closest weak and strong efficient projections in data envelopment analysis

Data Envelopment Analysis (DEA) is a nonparametric method for measuring the relative efficiency and performance of Decision Making Units (DMUs). Determining the least distance efficiency measure and thereby identifying the best reference point, is an important issue in recent DEA literature. In this paper, two alternative target setting models based on quadratically constrained programming (QCP), have been developed to allow for the low efficient DMUs to find the easiest way to improve theirs efficiency and reach the efficient boundary. One model seeks the closest weak efficient projection and the other suggests the most appropriate direction towards the strong efficient frontier surface. Both of these models provide the closest projection in one stage. Finally, a proposed problem is empirically checked by using recent data from thirty European airports.