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Uncertain data envelopment analysis with imprecisely observed inputs and outputs

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  • Waichon Lio

    (Tsinghua University)

  • Baoding Liu

    (Tsinghua University)

Abstract

Data envelopment analysis (DEA) is a powerful analytical tool in operations research and management for measuring and estimating the efficiency of decision-making units. Both the inputs and the outputs are assumed to be known constants in the classical DEA models. However, in many cases, those data (e.g., carbon emissions and social benefit) cannot be measured in a precise way. Therefore, in this article, the inputs and outputs are considered as uncertain variables and a new uncertain DEA model is introduced. The sensitivity and stability of the new model are also analyzed. Finally, a numerical example of the new model is documented.

Suggested Citation

  • Waichon Lio & Baoding Liu, 2018. "Uncertain data envelopment analysis with imprecisely observed inputs and outputs," Fuzzy Optimization and Decision Making, Springer, vol. 17(3), pages 357-373, September.
    • Handle: RePEc:spr:fuzodm:v:17:y:2018:i:3:d:10.1007_s10700-017-9276-x
    DOI: 10.1007/s10700-017-9276-x
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    References listed on IDEAS

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    3. Daniel Kahneman & Amos Tversky, 2013. "Prospect Theory: An Analysis of Decision Under Risk," World Scientific Book Chapters, in: Leonard C MacLean & William T Ziemba (ed.), HANDBOOK OF THE FUNDAMENTALS OF FINANCIAL DECISION MAKING Part I, chapter 6, pages 99-127, World Scientific Publishing Co. Pte. Ltd..
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    6. R. D. Banker & A. Charnes & W. W. Cooper, 1984. "Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis," Management Science, INFORMS, vol. 30(9), pages 1078-1092, September.
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    Cited by:

    1. Bao Jiang & Enxin Chi & Jian Li, 2022. "Uncertain Data Envelopment Analysis for Cross Efficiency Evaluation with Imprecise Data," Mathematics, MDPI, vol. 10(13), pages 1-9, June.
    2. Pourmahmoud, Jafar & Bagheri, Narges, 2023. "Uncertain Malmquist productivity index: An application to evaluate healthcare systems during COVID-19 pandemic," Socio-Economic Planning Sciences, Elsevier, vol. 87(PA).
    3. Xiangfeng Yang & Baoding Liu, 2019. "Uncertain time series analysis with imprecise observations," Fuzzy Optimization and Decision Making, Springer, vol. 18(3), pages 263-278, September.
    4. Enxin Chi & Bao Jiang & Luyao Peng & Yu Zhong, 2022. "Uncertain Network DEA Models with Imprecise Data for Sustainable Efficiency Evaluation of Decentralized Marine Supply Chain," Energies, MDPI, vol. 15(15), pages 1-16, July.
    5. Jianhua Ding & Zhiqiang Zhang, 2021. "Statistical inference on uncertain nonparametric regression model," Fuzzy Optimization and Decision Making, Springer, vol. 20(4), pages 451-469, December.
    6. Xinxin Wang & Zeshui Xu & Yong Qin, 2022. "Structure, trend and prospect of operational research: a scientific analysis for publications from 1952 to 2020 included in Web of Science database," Fuzzy Optimization and Decision Making, Springer, vol. 21(4), pages 649-672, December.
    7. Bao Jiang & Wenxue Feng & Jian Li, 2022. "Uncertain random data envelopment analysis for technical efficiency," Fuzzy Optimization and Decision Making, Springer, vol. 21(1), pages 1-20, March.
    8. Zhe Liu & Ying Yang, 2020. "Least absolute deviations estimation for uncertain regression with imprecise observations," Fuzzy Optimization and Decision Making, Springer, vol. 19(1), pages 33-52, March.
    9. Utsav Pandey & Sanjeet Singh, 2022. "Data envelopment analysis in hierarchical category structure with fuzzy boundaries," Annals of Operations Research, Springer, vol. 315(2), pages 1517-1549, August.
    10. Mohammad Jamshidi & Masoud Sanei & Ali Mahmoodirad & Farhad Hoseinzadeh Lotfi & Ghasem Tohidi, 2021. "Uncertain SBM data envelopment analysis model: A case study in Iranian banks," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(2), pages 2674-2689, April.

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