A super-efficiency model based on the directional distance function under a new direction
摘要
In order to solve the problem that the super-efficiency model based on directional distance function (DDF) is unable to handle negative data and infeasible under the assumption of variation in returns to scale (VRS), by choosing a suitable direction is an useful method. However, the newly chosen direction may lead to the emergence of weakly efficient projection point, which reduces the efficiency identification ability of the original model. In this paper, new direction is provided for the super-efficiency DDF model to overcome the problem of reducing the identification ability of the model. The new direction can provide strong efficient projection point for the inefficient decision making units (DMUs). The contributions of this paper are mainly as follows: (1) We find and explain the problem that the super-efficiency DDF model may have the issue of not being able to distinguish the efficiency of inefficient DMUs efficiently under some directions. (2) A new direction is provided under which the super-efficiency DDF model has a stronger ability to distinguish between the efficiency. (3) The new direction can also handle negative data while being able to ensure that the model is always feasible.