Data Envelopment Analysis with Preference Structure
Data Envelopment Analysis with Preference Structure
复制标题
DOI：
10.1057/jors.1996.12
复制
发表时间：
1996
影响因子：
3.6
通讯作者：
Joe Zhu
中科院分区：
文献类型：
--
作者：
Joe Zhu
在数据包络分析（DEA）中，考虑决策单元（DMU）或决策者对各种输入和输出的潜在调整的偏好是很重要的。本文在Russell测度的基础上，通过确定一组反映当前投入或产出水平潜在调整的相对可取程度的“偏好权重”，建立了一些加权非径向CCR模型。这些输入或输出调整可以小于或大于1;也就是说，该方法使某些输入实际上增加，或某些输出实际上减少。它示出的偏好结构规定固定的权重（虚拟乘数边界）或区域，无效的一些虚拟乘数，因此它产生优选的（有效的）输入和输出目标为每个DMU。除了提供首选的目标，该方法给出了一个标量效率得分为每个决策单元，以确保可比性。它还显示了我们的方法如何处理在DEA和测量配置和技术效率的不可控因素的具体情况。最后，将该方法应用于1991年中国14个沿海开放城市和4个经济特区的工业绩效。在这里，DEA/偏好结构模型细化了原来的DEA模型的结果，并消除了明显有效的决策单元。
It is important to consider the decision making unit (DMU)'s or decision maker's preference over the potential adjustments of various inputs and outputs when data envelopment analysis (DEA) is employed. On the basis of the so-called Russell measure, this paper develops some weighted non-radial CCR models by specifying a proper set of ‘preference weights’ that reflect the relative degree of desirability of the potential adjustments of current input or output levels. These input or output adjustments can be either less or greater than one; that is, the approach enables certain inputs actually to be increased, or certain outputs actually to be decreased. It is shown that the preference structure prescribes fixed weights (virtual multiplier bounds) or regions that invalidate some virtual multipliers and hence it generates preferred (efficient) input and output targets for each DMU. In addition to providing the preferred target, the approach gives a scalar efficiency score for each DMU to secure comparability. It is also shown how specific cases of our approach handle non-controllable factors in DEA and measure allocative and technical efficiency. Finally, the methodology is applied with the industrial performance of 14 open coastal cities and four special economic zones in 1991 in China. As applied here, the DEA/preference structure model refines the original DEA model's result and eliminates apparently efficient DMUs.