A lexicographic radial projection onto the efficient frontier in Data Envelopment Analysis

Korhonen, Pekka; Dehnokhalaji, Akram; Nasrabadi, Nasim

doi:10.1016/j.ejor.2017.08.050

Cited by 18 publications

(17 citation statements)

References 12 publications

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“…The main difference between Coelli (1998) and Korhonen et al (2018) is that the former considers radial oriented DEA models (first with input orientation and later with output orientation or vice versa) while the latter considers the non-oriented case in which inputs and outputs are improved simultaneously. In order to mathematically formulate the lexicographic 8 radial approach let us consider this latter case.…”

Section: Lexicographic Radial Approachmentioning

confidence: 99%

“…If that is not the case then the subsets of inputs and output dimensions that can be further improved are determined. Korhonen et al (2018) do this based on the optimal values of the input and output slacks as follows:…”

Section: Lexicographic Radial Approachmentioning

confidence: 99%

“…Cherchye and Van Puyenbroeck (2001) present an economic legitimation of this multi-stage methodology. Recently, Korhonen et al (2018) have proposed a similar lexicographic approach for radial oriented DEA models. There are, however, some differences between the methods in Coelli (1998) and Korhonen et al (2018).…”

Section: Introductionmentioning

confidence: 99%

“…Recently, Korhonen et al (2018) have proposed a similar lexicographic approach for radial oriented DEA models. There are, however, some differences between the methods in Coelli (1998) and Korhonen et al (2018). The latter consider nondiscretionary variables and the possibility of simultaneously changing any subset of the controllable inputs and output.…”

Section: Introductionmentioning

confidence: 99%

“…The procedure in Coelli (1998) is more robust in this regard but requires solving a separate linear program for each of the input and output dimensions that can, in principle, be improved. The lexicographic DDF approach proposed in this paper can use any directional vector and therefore includes the lexicographic radial approaches of Coelli (1998) and Korhonen et al (2018) as a special case. It also uses a different way of identifying the improvable input dimensions, based on a mixed-integer linear program (MILP) with a few binary variables, similar to the one used in Lozano and Calzada-Infante (2017).…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

DEA target setting using lexicographic and endogenous directional distance function approaches

Lozano

Soltani

2018

J Prod Anal

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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.

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Section: Lexicographic Radial Approachmentioning

confidence: 99%

Section: Lexicographic Radial Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

DEA target setting using lexicographic and endogenous directional distance function approaches

Lozano

Soltani

2018

J Prod Anal

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A compromise programming approach for target setting in DEA

2019

Self Cite

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This paper presents a new Data Envelopment Analysis (DEA) target setting approach that uses the Compromise Programming (CP) method of multiobjective optimization. This method computes the ideal point associated to each Decision Making Unit (DMU) and determines an ambitious, efficient target that is as close as possible (using an lp metric) to that ideal point. The specific cases p=1, p=2 and p= are separately discussed and analyzed. In particular, for p=1 and p=, a lexicographic optimization approach is proposed in order to guarantee uniqueness of the obtained target. The original CP method is translation invariant and has been adapted so that the proposed CP-DEA is also units invariant. An lp metric-based efficiency score is also defined for each DMU. The proposed CP-DEA approach can also be utilized in the presence of preference information, non-discretionary or integer variables and undesirable outputs. The proposed approach has been extensively compared with other DEA approaches on a dataset from the literature.

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A modified discrete Raiffa approach for efficiency assessment and target setting

Lozano

Soltani

2020

Ann Oper Res

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A lexicographic radial projection onto the efficient frontier in Data Envelopment Analysis

Cited by 18 publications

References 12 publications

DEA target setting using lexicographic and endogenous directional distance function approaches

DEA target setting using lexicographic and endogenous directional distance function approaches

A compromise programming approach for target setting in DEA

A modified discrete Raiffa approach for efficiency assessment and target setting

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