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Stochastic efficiency analysis with a reliability consideration

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  • Wei, Guiwu
  • Chen, Jian
  • Wang, Jiamin

Abstract

Stochastic Data Envelopment Analysis (DEA) models have been introduced in the literature to assess the performance of operating entities with random input and output data. A stochastic DEA model with a reliability constraint is proposed in this study that maximizes the lower bound of an entity׳s efficiency score with some pre-selected probability. We define the concept of stochastic efficiency and develop a solution procedure. The economic interpretations of the stochastic efficiency index are presented when the inputs and outputs of each entity follow a multivariate joint normal distribution.

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  • Wei, Guiwu & Chen, Jian & Wang, Jiamin, 2014. "Stochastic efficiency analysis with a reliability consideration," Omega, Elsevier, vol. 48(C), pages 1-9.
    • Handle: RePEc:eee:jomega:v:48:y:2014:i:c:p:1-9
    DOI: 10.1016/j.omega.2014.04.001
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    References listed on IDEAS

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    Cited by:

    1. Chen, Kun & Zhu, Joe, 2019. "Computational tractability of chance constrained data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 274(3), pages 1037-1046.
    2. Alireza Amirteimoori & Biresh K. Sahoo & Saber Mehdizadeh, 2023. "Data envelopment analysis for scale elasticity measurement in the stochastic case: with an application to Indian banking," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-36, December.
    3. Taylan G. Topcu & Konstantinos Triantis, 2022. "An ex-ante DEA method for representing contextual uncertainties and stakeholder risk preferences," Annals of Operations Research, Springer, vol. 309(1), pages 395-423, February.
    4. Abdullah Üstün, 2016. "Evaluating İstanbul’s disaster resilience capacity by data envelopment analysis," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 80(3), pages 1603-1623, February.
    5. P. Beraldi & M. E. Bruni, 2020. "Efficiency evaluation under uncertainty: a stochastic DEA approach," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 43(2), pages 519-538, December.
    6. Abdullah Korkut Üstün, 2016. "Evaluating İstanbul’s disaster resilience capacity by data envelopment analysis," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 80(3), pages 1603-1623, February.
    7. Sun, Qinghe & Chen, Li & Meng, Qiang, 2022. "Evaluating port efficiency dynamics: A risk-based approach," Transportation Research Part B: Methodological, Elsevier, vol. 166(C), pages 333-347.
    8. Kao, Chiang & Liu, Shiang-Tai, 2019. "Stochastic efficiency measures for production units with correlated data," European Journal of Operational Research, Elsevier, vol. 273(1), pages 278-287.
    9. Branda, Martin, 2015. "Diversification-consistent data envelopment analysis based on directional-distance measures," Omega, Elsevier, vol. 52(C), pages 65-76.
    10. Olesen, Ole B. & Petersen, Niels Christian, 2016. "Stochastic Data Envelopment Analysis—A review," European Journal of Operational Research, Elsevier, vol. 251(1), pages 2-21.

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