A modified slacks‐based ranking method handling negative data in data envelopment analysis. Issue 1 (23rd August 2018)

• Record Type: Journal Article
• Authors: Wei, Fangqing; Song, Jiayun; Jiao, Chuanya; Yang, Feng
• Is Part Of: Expert systems. Volume 36:Issue 1(2019)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2018-08-23
• Subjects: data envelopment analysis -- modified slacks‐based model -- negative data -- ranking; Expert systems (Computer science); 006.33
• DOI: 10.1111/exsy.12329
• Languages: English
• ISSNs: 0266-4720

Abstract

Abstract: Performance ranking for a set of comparable decision‐making units (DMUs) with multiple inputs and outputs is an important and often‐discussed topic in data envelopment analysis (DEA). Conventional DEA models distinguish efficient units from inefficient ones but cannot further discriminate the efficient units, which all have a 100% efficiency score. Another weakness of these models is that they cannot handle negative inputs and/or outputs. In this paper, a new modified slacks‐based measure is proposed that works in the presence of negative data and provides quantitative data that helps decision makers obtain a full ranking of DMUs in situations where other methods fail. In addition, the new method has the properties of unit invariance and translation invariance, and it can give targets for inefficient DMUs to guide them to achieve full efficiency. Two numerical examples are analysed to demonstrate the usefulness of the new method.