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Detecting two-dimensional projection-efficient units in data envelopment analysis under big data scenarios

Author

Listed:
  • Xu, Shuqi
  • Zhu, Qingyuan
  • Shen, Zhiyang
  • Vardanyan, Michael
  • Pan, Yinghao

Abstract

In the age of big data, traditional estimation methods may struggle to process large datasets efficiently. Ali (1993) laid the foundation for improving efficiency assessment using Data Envelopment Analysis (DEA). Building on this work, we demonstrate how to detect two-dimensional projection-efficient units. This is achieved by projecting the multidimensional DEA production frontier onto two-dimensional subspaces and utilizing slope analysis to identify key efficient units. These units are then linked to their full-dimensional counterparts to define projection-efficient units. We propose using these key efficient units as a preliminary step to speed up the identification of full-dimensional efficient units or to estimate the relative density of datasets. Simulations show that our method reduces computation time for the two fastest approaches by an average of 54.2 % across different datasets.

Suggested Citation

  • Xu, Shuqi & Zhu, Qingyuan & Shen, Zhiyang & Vardanyan, Michael & Pan, Yinghao, 2025. "Detecting two-dimensional projection-efficient units in data envelopment analysis under big data scenarios," European Journal of Operational Research, Elsevier, vol. 327(3), pages 957-970.
    • Handle: RePEc:eee:ejores:v:327:y:2025:i:3:p:957-970
    DOI: 10.1016/j.ejor.2025.05.053
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    References listed on IDEAS

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    1. Khezrimotlagh, Dariush & Zhu, Joe & Cook, Wade D. & Toloo, Mehdi, 2019. "Data envelopment analysis and big data," European Journal of Operational Research, Elsevier, vol. 274(3), pages 1047-1054.
    2. Dariush Khezrimotlagh, 2021. "Parallel Processing and Large-Scale Datasets in Data Envelopment Analysis," International Series in Operations Research & Management Science, in: Joe Zhu & Vincent Charles (ed.), Data-Enabled Analytics, pages 159-174, Springer.
    3. 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.
    4. Chu, Junfei & Rui, Yuting & Khezrimotlagh, Dariush & Zhu, Joe, 2024. "A general computational framework and a hybrid algorithm for large-scale data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 316(2), pages 639-650.
    5. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    6. Ali, Agha Iqbal, 1993. "Streamlined computation for data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 64(1), pages 61-67, January.
    7. Richard Barr & Matthew Durchholz, 1997. "Parallel and hierarchical decomposition approaches for solving large-scale Data Envelopment Analysis models," Annals of Operations Research, Springer, vol. 73(0), pages 339-372, October.
    8. Victor V. Podinovski, 2022. "Variable and Constant Returns-to-Scale Production Technologies with Component Processes," Operations Research, INFORMS, vol. 70(2), pages 1238-1258, March.
    9. Wen-Chih Chen & Sheng-Yung Lai, 2017. "Determining radial efficiency with a large data set by solving small-size linear programs," Annals of Operations Research, Springer, vol. 250(1), pages 147-166, March.
    10. Murty, Sushama & Robert Russell, R. & Levkoff, Steven B., 2012. "On modeling pollution-generating technologies," Journal of Environmental Economics and Management, Elsevier, vol. 64(1), pages 117-135.
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