Efficiency evaluation with data uncertainty

Jie Wu, Lulu Shen, Ganggang Zhang (), Zhixiang Zhou and Qingyuan Zhu
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Jie Wu: University of Science and Technology of China
Lulu Shen: University of Science and Technology of China
Ganggang Zhang: University of Science and Technology of China
Zhixiang Zhou: Hefei University of Technology
Qingyuan Zhu: Nanjing University of Aeronautics and Astronautics

Annals of Operations Research, 2024, vol. 339, issue 3, No 11, 1379-1403

Abstract: Abstract As one of the most popular techniques for performance evaluation, Data Envelopment Analysis (DEA) has been widely applied in many areas. However, the self-evaluation used in DEA leaves it open to much criticism. Moreover, most researchers have ignored the fact that reality abounds with uncertainty and have assumed that the data used for evaluation is deterministic and accurate. Both assumptions make it difficult to evaluate the efficiency of real-world production processes correctly and reasonably. In this paper, we propose a series of robust cross-efficiency (RCE) models based on robust optimization theory and cross-efficiency to deal with these problems. First of all, the proposed RCE models allow the conservatism level to be adjusted easily to suit the attitude of the decision-maker towards uncertainty. In addition, the RCE models have better discrimination power than the existing robust CCR models. We present two applications to demonstrate the effectiveness and stability of our models.

Keywords: Data envelopment analysis; Cross-efficiency; Robust optimization; Data uncertainty (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10479-022-04636-0

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