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.

Publication DOI: https://doi.org/10.1007/s10479-019-03486-7
Divisions: Aston Business School > Operations & Information Management
Aston Business School
Additional Information: © 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.
Uncontrolled Keywords: Compromise programming,Data envelopment analysis,Ideal point,Target setting,l metric,Decision Sciences(all),Management Science and Operations Research
Full Text Link:
Related URLs: http://link.spr ... 479-019-03486-7 (Publisher URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2019-11-28
Published Online Date: 2019-11-28
Accepted Date: 2019-11-28
Authors: Lozano, Sebastián
Soltani, Narges
Dehnokhalaji, Akram ( 0000-0002-2751-0719)

Download

[img]

Version: Accepted Version

Access Restriction: Restricted to Repository staff only until 28 November 2020.


Export / Share Citation


Statistics

Additional statistics for this record