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Decomposing the efficient frontier of the DEA production possibility set into a smallest number of convex polyhedrons by mixed integer programming

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  • Fukuyama, Hirofumi
  • Sekitani, Kazuyuki

Abstract

This paper extends the works by Olesen and Petersen (2003), Russell and Schworm (2006) and Cooper et al. (2007) about describing the efficient frontier of a production possibility set by the intersection of a finite number of closed halfspaces, in several ways. First, we decompose the efficient frontier into a smallest number of convex polyhedrons, or equivalently into a new class of efficient faces, called maximal efficient faces (MEFs). Second, we show how to identify all MEFs even if full dimensional efficient faces do not exist. Third, by applying the MEF decomposition to various real-world data sets, we demonstrate the validity of the MEF decomposition and how it can contribute to the DEA literature. Finally, we illustrate how to use the identified MEFs in practice.

Suggested Citation

  • Fukuyama, Hirofumi & Sekitani, Kazuyuki, 2012. "Decomposing the efficient frontier of the DEA production possibility set into a smallest number of convex polyhedrons by mixed integer programming," European Journal of Operational Research, Elsevier, vol. 221(1), pages 165-174.
  • Handle: RePEc:eee:ejores:v:221:y:2012:i:1:p:165-174
    DOI: 10.1016/j.ejor.2012.02.035
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    Cited by:

    1. Fukuyama, Hirofumi & Maeda, Yasunobu & Sekitani, Kazuyuki & Shi, Jianming, 2014. "Input–output substitutability and strongly monotonic p-norm least distance DEA measures," European Journal of Operational Research, Elsevier, vol. 237(3), pages 997-1007.
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    4. Panagiotis Ravanos & Giannis Karagiannis, 2022. "In search for the Most Preferred Solution in Value Efficiency Analysis," Discussion Paper Series 2022_05, Department of Economics, University of Macedonia, revised Jul 2022.
    5. Borrás, Fernando & Ruiz, José L. & Sirvent, Inmaculada, 2023. "Peer evaluation through cross-efficiency based on reference sets," Omega, Elsevier, vol. 114(C).
    6. 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.
    7. Hirofumi Fukuyama & Hiroya Masaki & Kazuyuki Sekitani & Jianming Shi, 2014. "Distance optimization approach to ratio-form efficiency measures in data envelopment analysis," Journal of Productivity Analysis, Springer, vol. 42(2), pages 175-186, October.
    8. Giannis Karagiannis & Panagiotis Ravanos, 2023. "On Value Efficiency Analysis and Cone-Ratio Data Envelopment Analysis models," Discussion Paper Series 2023_03, Department of Economics, University of Macedonia, revised Mar 2023.
    9. Zhu, Qingyuan & Aparicio, Juan & Li, Feng & Wu, Jie & Kou, Gang, 2022. "Determining closest targets on the extended facet production possibility set in data envelopment analysis: Modeling and computational aspects," European Journal of Operational Research, Elsevier, vol. 296(3), pages 927-939.
    10. Ando, Kazutoshi & Minamide, Masato & Sekitani, Kazuyuki & Shi, Jianming, 2017. "Monotonicity of minimum distance inefficiency measures for Data Envelopment Analysis," European Journal of Operational Research, Elsevier, vol. 260(1), pages 232-243.
    11. Amineh Ghazi & Farhad Hosseinzadeh Lotfı & Masoud Sanei, 2022. "Finding the strong efficient frontier and strong defining hyperplanes of production possibility set using multiple objective linear programming," Operational Research, Springer, vol. 22(1), pages 165-198, March.
    12. Amineh Ghazi & Farhad Hosseinzadeh Lotfi & Masoud Sanei, 2020. "Hybrid efficiency measurement and target setting based on identifying defining hyperplanes of the PPS with negative data," Operational Research, Springer, vol. 20(2), pages 1055-1092, June.
    13. Panagiotis Ravanos & Giannis Karagiannis, 2022. "In search for the most preferred solution in value efficiency analysis," Journal of Productivity Analysis, Springer, vol. 58(2), pages 203-220, December.
    14. Lozano, Sebastián & Khezri, Somayeh, 2021. "Network DEA smallest improvement approach," Omega, Elsevier, vol. 98(C).
    15. A. Ghazi & F. Hosseinzadeh Lotfi, 2023. "Marginal rates in DEA using defining hyperplanes of PPS with CRS technology," Operational Research, Springer, vol. 23(1), pages 1-37, March.
    16. Sekitani, Kazuyuki & Zhao, Yu, 2021. "Performance benchmarking of achievements in the Olympics: An application of Data Envelopment Analysis with restricted multipliers," European Journal of Operational Research, Elsevier, vol. 294(3), pages 1202-1212.
    17. Hirofumi Fukuyama & Yong Tan, 2021. "Corporate social behaviour: Is it good for efficiency in the Chinese banking industry?," Annals of Operations Research, Springer, vol. 306(1), pages 383-413, November.
    18. Fujii, Hidemichi & Managi, Shunsuke & Matousek, Roman, 2014. "Indian bank efficiency and productivity changes with undesirable outputs: A disaggregated approach," Journal of Banking & Finance, Elsevier, vol. 38(C), pages 41-50.
    19. Hirofumi Fukuyama & William Weber, 2015. "Measuring Japanese bank performance: a dynamic network DEA approach," Journal of Productivity Analysis, Springer, vol. 44(3), pages 249-264, December.
    20. Ole Bent Olesen & Niels Christian Petersen & Victor V. Podinovski, 2022. "The structure of production technologies with ratio inputs and outputs," Journal of Productivity Analysis, Springer, vol. 57(3), pages 255-267, June.
    21. 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).
    22. Fukuyama, Hirofumi & Tsionas, Mike & Tan, Yong, 2024. "The impacts of innovation and trade openness on bank market power: The proposal of a minimum distance cost function approach and a causal structure analysis," European Journal of Operational Research, Elsevier, vol. 312(3), pages 1178-1194.
    23. Zhu, Qingyuan & Wu, Jie & Ji, Xiang & Li, Feng, 2018. "A simple MILP to determine closest targets in non-oriented DEA model satisfying strong monotonicity," Omega, Elsevier, vol. 79(C), pages 1-8.

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