A directional distance based super-efficiency DEA model handling negative data

Ruiyue Lin and Zhiping Chen ()
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Ruiyue Lin: Xi’an Jiaotong University
Zhiping Chen: Xi’an Jiaotong University

Journal of the Operational Research Society, 2017, vol. 68, issue 11, 1312-1322

Abstract: Abstract This paper develops a new radial super-efficiency data envelopment analysis (DEA) model, which allows input–output variables to take both negative and positive values. Compared with existing DEA models capable of dealing with negative data, the proposed model can rank the efficient DMUs and is feasible no matter whether the input–output data are non-negative or not. It successfully addresses the infeasibility issue of both the conventional radial super-efficiency DEA model and the Nerlove–Luenberger super-efficiency DEA model under the assumption of variable returns to scale. Moreover, it can project each DMU onto the super-efficiency frontier along a suitable direction and never leads to worse target inputs or outputs than the original ones for inefficient DMUs. Additional advantages of the proposed model include monotonicity, units invariance and output translation invariance. Two numerical examples demonstrate the practicality and superiority of the new model.

Keywords: data envelopment analysis; negative data; directional distance function; super-efficiency; infeasibility (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (14)

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DOI: 10.1057/s41274-016-0137-8

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