Abstract
Data Envelopment Analysis (DEA) is a nonparametric method for measuring the relative efficiency and performance of Decision Making Units (DMUs). Traditionally, there are two issues regarding the DEA simultaneously i.e., the identification of a reference point on the efficient boundary of the production possibility set (PPS) and the use of some measures of distance from the unit under assessment to the efficient frontier. Due to its importance, in this paper, two alternative target setting models were developed to allow for lowefficient DMUs find the easiest way to improve its efficiency and reach to the efficient boundary. One seeks the closest weak efficient projection and the other suggests the most appropriate direction towards the strong efficient frontier surface. Both of these models provides the closest projection in one stage. Finally, a proposed problem is empirically checked by using a recent data related to 30 European airports.
Highlights
In this regard, by using the weighting scheme provided by the dual prices for calculating a composite point on the frontier, Sherman and Gold [24] determined a projection onto the efficient frontier
Amirteimoori and Kordrostami [1] proposed an Euclidean distance based measure of efficiency by stating that it searches the shortest path to the efficient frontier of PPS
Our models have a few constraints in comparison with some similar models in obtaining the closest efficient targets in one stage
Summary
We first present some preliminaries about DEA. Consider a set of n observed DMUs, {DMU1,DMU2,. . .,DMUn}. Recently some authors argue that the distance should be minimized instead of maximized in order to find targets as similar as possible to the inefficient DMU under evaluation The idea behind this viewpoint is that the closer the efficient projection to the DMU under evaluation, the easier it is to reach the efficient frontier with less variation in its inputs and outputs. In this regard, on one hand, some researchers focus their works on finding all defining hyperplanes to obtain projection points which is NP-hard from computational point of view [17,18]. All of the constraints in these models are linear except one
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