A compromise programming approach for target setting in DEA

Sebastián Lozano; Narges Soltani; Akram Dehnokhalaji

doi:10.1007/s10479-019-03486-7

A compromise programming approach for target setting in DEA

Sebastián Lozano^*, Narges Soltani, Akram Dehnokhalaji

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

Abstract

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.

Original language	English
Pages (from-to)	363–390
Journal	Annals of Operations Research
Volume	288
Early online date	28 Nov 2019
DOIs	https://doi.org/10.1007/s10479-019-03486-7
Publication status	Published - May 2020

Bibliographical note

© Springer Nature B.V. 2019. The final publication is available at Springer via http://dx.doi.org/10.1007/s10479-019-03486-7

Funding: Spanish Ministry of Economy, Industry and Competitiveness and the European Regional Development Fund, Grant DPI2017-85343-P. Ministry of Science, Research and Technology of the Islamic Republic of Iran.

Keywords

Compromise programming
Data envelopment analysis
Ideal point
Target setting
l metric

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10.1007/s10479-019-03486-7

A compromise programming approach for target setting in DEA
© Springer Nature B.V. 2019. The final publication is available at Springer via http://dx.doi.org/10.1007/s10479-019-03486-7
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title = "A compromise programming approach for target setting in DEA",

abstract = "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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author = "Sebasti{\'a}n Lozano and Narges Soltani and Akram Dehnokhalaji",

note = "{\textcopyright} Springer Nature B.V. 2019. The final publication is available at Springer via http://dx.doi.org/10.1007/s10479-019-03486-7 Funding: Spanish Ministry of Economy, Industry and Competitiveness and the European Regional Development Fund, Grant DPI2017-85343-P. Ministry of Science, Research and Technology of the Islamic Republic of Iran.",

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doi = "10.1007/s10479-019-03486-7",

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AU - Soltani, Narges

AU - Dehnokhalaji, Akram

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N2 - 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.

AB - 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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KW - Ideal point

KW - Target setting

KW - l metric

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ER -

A compromise programming approach for target setting in DEA

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Keywords

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