Sequential benchmark selection on Pareto-efficient frontiers with endogenous directions
Sequential benchmark selection on Pareto-efficient frontiers with endogenous directions
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DOI：
10.1080/01605682.2021.2020180
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发表时间：
2022-01
影响因子：
3.6
通讯作者：
Xiangyang Tao;Qingxian An;Beibei Xiong;Sijia Cai
中科院分区：
文献类型：
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作者：
Xiangyang Tao;Qingxian An;Beibei Xiong;Sijia Cai
方向距离函数（DDF）是性能评估和基准选择中的一种常用技术。然而，DDF在基准选择中的一个缺点在于，由于方向向量的选择，它无法确保Pareto有效的基准。在当前的文章中，我们开发了一种方法来确定内生方向，以保证选择的基准在帕累托有效的前沿序列基准选择。该方法综合了DDF和上下文相关的数据挖掘分析（CD-DEA）。内生方向是由投入和产出的可能减少和扩大之间的“权衡”决定的。我们证明了所选择的基准是基于DEA的强有效（帕累托有效）和仿射不变。此外，我们概念化的努力实现选定的基准作为基准指数。我们证明，基准指标可以描述的DDF和欧氏距离之间的评价决策单元和基准。几个性能的基准指数，即积极的，弱单调，平移不变，和参考集依赖，证明。最后，对我国交通运输系统的序贯基准选择进行了详细的分析。
Abstract Directional distance function (DDF) has been a popular technique in performance evaluation and benchmark selection. However, one drawback of DDF in benchmark selection lies in its inability to ensure Pareto-efficient benchmarks because of the selection of directional vectors. In the current article, we develop an approach to identify endogenous directions to guarantee selected benchmarks on Pareto-efficient frontiers in sequential benchmark selection. The approach synthesizes DDF and context-dependent data envelopment analysis (CD-DEA). The endogenous directions are determined by a “trade-off” between potential reductions and expansions of inputs and outputs, respectively. We prove that the selected benchmarks are DEA-based strongly efficient (Pareto efficient) and affine invariant. Moreover, we conceptualize the effort of realizing the selected benchmarks as a benchmark index. We demonstrate that the benchmark index can be described by DDF and Euclidean distance between the evaluated decision-making unit and its benchmark. Several properties of the benchmark index, namely positive, weak monotonic, translation invariant, and reference-set dependent, are proven. Finally, a detailed analysis is conducted to select sequential benchmarks for China’s transportation system.