DEA target setting using lexicographic and endogenous directional distance function approaches

Sebastián Lozano () and Narges Soltani
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Sebastián Lozano: University of Seville
Narges Soltani: Kharazmi University

Journal of Productivity Analysis, 2018, vol. 50, issue 1, No 4, 55-70

Abstract: Abstract Directional Distance Function (DDF) is an approach often used in data envelopment analysis (DEA) due to its clear interpretation and to the flexibility provided by the possibility of choosing the projection direction towards the efficient frontier. In this paper two new DDF approaches are considered. The first one uses an exogenous directional vector and a multi-stage methodology that at each step uses the projection along the input and output dimensions of the directional vector that can be improved. This lexicographic DDF approach also computes a directional efficiency score and a directional inefficiency indicator for each input and output variable. The second approach is a non-linear optimization model that endogenously determines the directional vector so that the smallest improvement required to reach the efficient frontier is computed.

Keywords: Data envelopment analysis; Directional distance function; Multi-stage methodology; Directional efficiency score; Endogenous directional vector (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (8)

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DOI: 10.1007/s11123-018-0534-x

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