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Computing gradient-based stepwise benchmarking paths

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  • Lozano, Sebastián
  • Calzada-Infante, Laura

Abstract

In this paper, a new stepwise benchmarking approach is presented. It is based on the concept of efficiency field potential given by a continuous and differentiable function that decreases monotonously as the amount of inputs consumed is reduced and the amount of outputs produced is increased. A gradient-based stepwise efficiency improvement method is proposed and the graphical interpretation of the continuous gradient-based trajectories is shown. A minimum potential DEA model is also formulated. The proposed approach is units invariant and can take into account preference structure, non-discretionary variables and undesirable outputs. The proposed method has been applied to an organic farming dataset.

Suggested Citation

  • Lozano, Sebastián & Calzada-Infante, Laura, 2018. "Computing gradient-based stepwise benchmarking paths," Omega, Elsevier, vol. 81(C), pages 195-207.
    • Handle: RePEc:eee:jomega:v:81:y:2018:i:c:p:195-207
    DOI: 10.1016/j.omega.2017.11.002
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    Cited by:

    1. Monge, Juan F. & Ruiz, José L., 2023. "Setting closer targets based on non-dominated convex combinations of Pareto-efficient units: A bi-level linear programming approach in Data Envelopment Analysis," European Journal of Operational Research, Elsevier, vol. 311(3), pages 1084-1096.
    2. Sebastián Lozano & Narges Soltani & Akram Dehnokhalaji, 2020. "A compromise programming approach for target setting in DEA," Annals of Operations Research, Springer, vol. 288(1), pages 363-390, May.
    3. Sebastián Lozano & Narges Soltani, 2020. "A modified discrete Raiffa approach for efficiency assessment and target setting," Annals of Operations Research, Springer, vol. 292(1), pages 71-95, September.
    4. An, Qingxian & Tao, Xiangyang & Xiong, Beibei, 2021. "Benchmarking with data envelopment analysis: An agency perspective," Omega, Elsevier, vol. 101(C).
    5. Sebastián Lozano & Narges Soltani, 2018. "DEA target setting using lexicographic and endogenous directional distance function approaches," Journal of Productivity Analysis, Springer, vol. 50(1), pages 55-70, October.
    6. Kadziński, Miłosz & Stamenković, Mladen & Uniejewski, Maciej, 2022. "Stepwise benchmarking for multiple criteria sorting," Omega, Elsevier, vol. 108(C).
    7. Lozano, Sebastián & Khezri, Somayeh, 2021. "Network DEA smallest improvement approach," Omega, Elsevier, vol. 98(C).
    8. Ramón, Nuria & Ruiz, José L. & Sirvent, Inmaculada, 2020. "Cross-benchmarking for performance evaluation: Looking across best practices of different peer groups using DEA," Omega, Elsevier, vol. 92(C).
    9. An, Qingxian & Zhang, Qiaoyu & Tao, Xiangyang, 2023. "Pay-for-performance incentives in benchmarking with quasi S-shaped technology," Omega, Elsevier, vol. 118(C).
    10. Ji, Zhiyong & Wu, Xianhua & Chen, Xueli & Zhou, Wenzhuo & Song, Malin, 2023. "Finding green performance targets globally closest to management goals for ports experiencing similar circumstances," Resources Policy, Elsevier, vol. 85(PB).

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