两种改进的超效DEA模型

Two Improved Super-Efficient DEA Models

对DEA有效决策单元不能排序是传统DEA模型的一大缺点。该问题也成为DEA领域研究的热点。AP模型是最常用的区分DEA有效决策单元效率值大小的超效模型,但在实际应用中却出现了无解或解不稳定的情况。国外学者新近提出的MAJ模型、LJK模型则有效解决了这两个问题,但却存在模型区分能力不强的缺点。本文分析了这两种模型缺点产生的原因、予以改进并给出理论证明。标准数据集的实验显示改进效果显著,尤其是改进后的LJK模型表现优异。

The traditional Data Envelopment Analysis （DEA） models are unable to discriminate between efficient DMUs. This issue has become the interest of many DEA researchers, AP model is a popular super --efficient model for ranking efficient DMUs, but it has infeasibility and instability problems. MAJ model and LJK model recently proposed can overcome these deficiencies but their ability to distinguish efficient DMUs is not strong. This paper analyzes the reason of these two models＇ shortcomings, then improves them and provides theoretical proofs. Through two standard numerical examples, we find the improved models perform better than old ones, especially the improved LJK model.