Efficiency evaluation with regret-rejoice cross-efficiency DEA models under the distributed linguistic environment. (July 2022)

• Record Type: Journal Article
• Authors: Jin, Feifei; Cai, Yuhang; Pedrycz, Witold; Liu, Jinpei
• Is Part Of: Computers & industrial engineering. Volume 169(2022)
• Publication Date: 2022-07
• Subjects: Data envelopment analysis -- Regret theory -- Cross-efficiency evaluation -- Linguistic distribution; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2022.108281
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: A PLDEA model is constructed to generate the self-evaluation efficiency values of DMUs. A RCEE model is developed to evaluate the cross-efficiencies of DMUs A RSEE model is proposed to generate the complete ranking of the DMUs. A numerical example is provided to show the advantages of the proposed models. Abstract: Data envelopment analysis (DEA) is an effective mathematical method for evaluating the efficiencies of decision-making units (DMUs). However, in the process of cross-efficiency evaluation, DEA models commonly neglect the regret aversion psychological characteristics of decision makers (DMs). Therefore, this paper focuses on the construction of regret-rejoice cross-efficiency linguistic distribution DEA (RCE-LDDEA) method with regret theory, in which the input and output data by means of linguistic distributions and the regret aversion psychological characteristics of DMs are considered. First, a linguistic distribution DEA model is proposed to derive the self-evaluation efficiencies of DMUs with linguistic distribution evaluation information. Then, based on regret theory, a regret-rejoice cross-efficiency evaluation (RCEE) model is developed to evaluate the cross-efficiencies of DMUs. Subsequently, based on the regret-rejoice super-efficiency evaluation (RSEE) model, a RCE-LDDEA method is designed to generate the complete ranking of DMUs. Finally, an example for evaluating performance of 10 public hospitals in China is provided to illustrate the implementation of the proposed RCE-LDDEA method. The stability and advantages of the proposed RCE-LDDEA method is performed by sensitivity analysis and comparative analysis.