A robust cross-efficiency data envelopment analysis model with undesirable outputs
作者：
Highlights：
• Data envelopment analysis (DEA) is challenged by imprecise, uncertain, and stochastic data.
• Two DEA adaptations (interval and robust) are developed with uncertain data and undesirable outputs.
• An epsilon-based robust interval cross-efficiency model is extended.
• An example and a real-world application are presented to compare our method with an interval method.
• The ability of our method to improve discernibility among DMUs is demonstrated.
摘要
•Data envelopment analysis (DEA) is challenged by imprecise, uncertain, and stochastic data.•Two DEA adaptations (interval and robust) are developed with uncertain data and undesirable outputs.•An epsilon-based robust interval cross-efficiency model is extended.•An example and a real-world application are presented to compare our method with an interval method.•The ability of our method to improve discernibility among DMUs is demonstrated.
论文关键词：Data envelopment analysis,Uncertain data,Undesirable outputs,Cross-efficiency evaluation,Robust optimization,Interval approach
论文评审过程：Received 8 August 2020, Revised 3 October 2020, Accepted 11 October 2020, Available online 20 October 2020, Version of Record 10 February 2021.
论文官网地址：https://doi.org/10.1016/j.eswa.2020.114117
