Least-distance approach for efficiency analysis: A framework for nonlinear DEA models
Least-distance approach for efficiency analysis: A framework for nonlinear DEA models
复制标题
DOI：
10.1016/j.ejor.2022.09.001
复制
发表时间：
2022-09
期刊：
影响因子：
--
通讯作者：
Kazuyuki Sekitani;Yu Zhao
中科院分区：
文献类型：
--
作者：
Kazuyuki Sekitani;Yu Zhao
提出了一类非线性数据包络分析（DEA）模型，包括Russell图测度（RM）、BRWZ测度、松弛基测度（SBM）和几何距离函数（GDF）的变体.基于线性规划，这类DEA模型提供了一个单调的最大效率测度和一个有效目标，该目标距离生产可能性集的弱有效前沿曼哈顿距离最小.我们表明，在这个类中的最大效率的措施，可以明确表示为一个递减函数的最小曼哈顿距离。此外，通过对这类DEA模型加入一定的一致性权重限制，使得最大有效性测度满足强单调性。
We propose a class of nonlinear data envelopment analysis (DEA) models, including variants of the Russell graph measure (RM), BRWZ measure, slack-based measure (SBM), and geometric distance function (GDF). Based on linear programming, this class of DEA models provides a monotonic maximum efficiency measure and an efficient target that achieves the least Manhattan distance from the weakly efficient frontier of the production possibility set. We show that the maximum efficiency measure in this class can be explicitly expressed as a decreasing function of the least Manhattan distance. Furthermore, by adding certain consistent weight restrictions to this class of DEA models, the maximum efficiency measures satisfy strong monotonicity.