Least-distance approach for efficiency analysis: A framework for nonlinear DEA models
提出一类非线性数据包络分析模型，包括Russell图测度、BRWZ测度、松弛测度和几何距离函数的变体，基于线性规划提供单调最大效率测度，并给出达到弱有效前沿最小曼哈顿距离的有效目标。
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.