Data Envelopment Analysis with Robust and Closest Targets:Integrating Full-Dimensional Efficient Facets for Risk-Resilient Benchmarking
Year: 2025
Type: preprint
Abstract: As the external environment become increasingly volatile and unpredictable, the selection of benchmarking targets in data envelopment analysis should account for their ability to consider risks; however, this aspect has not received sufficient attention. We propose a robust benchmarking target defined by the intersection of the maximum number of full-dimensional efficient facets, each representing a unique marginal substitution relationship. These targets can serve as robust projections for decision making units that are lacking prior risk information because they incorporate the maximum number of marginal substitution relationships. This enables decision makers to adjust their production through these relationships, thereby maximizing the likelihood of achieving globally optimal outcomes. Furthermore, we propose a novel, well-defined efficiency measure based on robust and closest targets. Finally, we demonstrate the application of the proposed measure using a dataset comprising 38 universities from China's 985 Project.
Source: arXiv (Cornell University)
Authors: Huang, Xiuquan, Wang, Xi, Zhang, Tao, Xu, Xiaocang, Emrouznejad, Ali
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W4417536123
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