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A compromise programming approach for target setting in DEA

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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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Acknowledgements

This research was carried out with the financial support of the Spanish Ministry of Economy, Industry and Competitiveness and the European Regional Development Fund, Grant DPI2017-85343-P. Narges Soltani acknowledges the support of a grant from the Ministry of Science, Research and Technology of the Islamic Republic of Iran.

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Authors and Affiliations

  1. Department of Industrial Management, Escuela Superior de Ingenieros, University of Seville, Camino de los Descubrimientos, s/n, 41092, Seville, Spain

    Sebastián Lozano

  2. School of Information Technology, York University, Toronto, Canada

    Narges Soltani

  3. Operations and Information Management Department, Aston Business School, Aston University, Birmingham, B4 7ET, UK

    Akram Dehnokhalaji

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  1. Sebastián Lozano
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  2. Narges Soltani
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  3. Akram Dehnokhalaji
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Correspondence to Sebastián Lozano.

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Lozano, S., Soltani, N. & Dehnokhalaji, A. A compromise programming approach for target setting in DEA. Ann Oper Res 288, 363–390 (2020). https://doi.org/10.1007/s10479-019-03486-7

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