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Determining a sequence of targets in DEA

Author

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  • S Lozano

    (Universidad de Sevilla)

  • G Villa

    (Universidad de Sevilla)

Abstract

Data Envelopment Analysis (DEA) can be used for assessing the relative efficiency of a number of operating units, finding, for each inefficient unit, a target operating point lying on the efficient frontier. Most DEA models project an inefficient unit onto a most distant target, which makes its attainment more difficult. In this paper, we advocate determining a sequence of targets, each one within an appropriate, short distance of the preceding. The proposed Constant Returns to Scale approach has two interesting features: (a) the sequence of targets ends in the efficient frontier and (b) the final, efficient target is generally closer to the original unit than the one-step projection is.

Suggested Citation

  • S Lozano & G Villa, 2005. "Determining a sequence of targets in DEA," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(12), pages 1439-1447, December.
    • Handle: RePEc:pal:jorsoc:v:56:y:2005:i:12:d:10.1057_palgrave.jors.2601964
    DOI: 10.1057/palgrave.jors.2601964
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    References listed on IDEAS

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    1. Boaz Golany & Sten Thore, 1997. "Restricted best practice selection in DEA: An overview with a case study evaluating the socio-economic performance of nations," Annals of Operations Research, Springer, vol. 73(0), pages 117-140, October.
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    5. Maria Silva Portela & Pedro Borges & Emmanuel Thanassoulis, 2003. "Finding Closest Targets in Non-Oriented DEA Models: The Case of Convex and Non-Convex Technologies," Journal of Productivity Analysis, Springer, vol. 19(2), pages 251-269, April.
    6. Frances Frei & Patrick Harker, 1999. "Projections Onto Efficient Frontiers: Theoretical and Computational Extensions to DEA," Journal of Productivity Analysis, Springer, vol. 11(3), pages 275-300, June.
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    Cited by:

    1. Aparicio, Juan & Cordero, Jose M. & Pastor, Jesus T., 2017. "The determination of the least distance to the strongly efficient frontier in Data Envelopment Analysis oriented models: Modelling and computational aspects," Omega, Elsevier, vol. 71(C), pages 1-10.
    2. Aparicio, Juan & Garcia-Nove, Eva M. & Kapelko, Magdalena & Pastor, Jesus T., 2017. "Graph productivity change measure using the least distance to the pareto-efficient frontier in data envelopment analysis," Omega, Elsevier, vol. 72(C), pages 1-14.
    3. Kadziński, Miłosz & Stamenković, Mladen & Uniejewski, Maciej, 2022. "Stepwise benchmarking for multiple criteria sorting," Omega, Elsevier, vol. 108(C).
    4. Fukuyama, Hirofumi & Matousek, Roman & Tzeremes, Nickolaos G., 2023. "Estimating the degree of firms’ input market power via data envelopment analysis: Evidence from the global biotechnology and pharmaceutical industry," European Journal of Operational Research, Elsevier, vol. 305(2), pages 946-960.
    5. Fukuyama, Hirofumi & Matousek, Roman & Tzeremes, Nickolaos G., 2022. "Bank production with nonperforming loans: A minimum distance directional slack inefficiency approach," Omega, Elsevier, vol. 113(C).
    6. Ruiz, José L. & Sirvent, Inmaculada, 2019. "Performance evaluation through DEA benchmarking adjusted to goals," Omega, Elsevier, vol. 87(C), pages 150-157.
    7. Ruiz, José L. & Segura, José V. & Sirvent, Inmaculada, 2015. "Benchmarking and target setting with expert preferences: An application to the evaluation of educational performance of Spanish universities," European Journal of Operational Research, Elsevier, vol. 242(2), pages 594-605.
    8. Lozano, Sebastián & Khezri, Somayeh, 2021. "Network DEA smallest improvement approach," Omega, Elsevier, vol. 98(C).
    9. Lozano, Sebastián & Calzada-Infante, Laura, 2018. "Computing gradient-based stepwise benchmarking paths," Omega, Elsevier, vol. 81(C), pages 195-207.
    10. Aparicio, Juan & Pastor, Jesus T., 2014. "Closest targets and strong monotonicity on the strongly efficient frontier in DEA," Omega, Elsevier, vol. 44(C), pages 51-57.
    11. Petrović, Marijana & Bojković, Nataša & Stamenković, Mladen & Anić, Ivan, 2018. "Supporting performance appraisal in ELECTRE based stepwise benchmarking model," Omega, Elsevier, vol. 78(C), pages 237-251.
    12. Lee, Chia-Yen, 2018. "Mixed-strategy Nash equilibrium in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 266(3), pages 1013-1024.
    13. Ghahraman, Abaghan & Prior, Diego, 2016. "A learning ladder toward efficiency: Proposing network-based stepwise benchmark selection," Omega, Elsevier, vol. 63(C), pages 83-93.

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