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A new chance-constrained DEA model with birandom input and output data

Author

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  • Madjid Tavana

    (1] La Salle University, Philadelphia, USA[2] University of Paderborn, Paderborn, Germany)

  • Rashed Khanjani Shiraz

    (Tabriz University, Tabriz, Iran)

  • Adel Hatami-Marbini

    (Université catholique de Louvain, Louvain-la-Neuve, Belgium)

Abstract

The purpose of conventional Data Envelopment Analysis (DEA) is to evaluate the performance of a set of firms or Decision-Making Units using deterministic input and output data. However, the input and output data in the real-life performance evaluation problems are often stochastic. The stochastic input and output data in DEA can be represented with random variables. Several methods have been proposed to deal with the random input and output data in DEA. In this paper, we propose a new chance-constrained DEA model with birandom input and output data. A super-efficiency model with birandom constraints is formulated and a non-linear deterministic equivalent model is obtained to solve the super-efficiency model. The non-linear model is converted into a model with quadratic constraints to solve the non-linear deterministic model. Furthermore, a sensitivity analysis is performed to assess the robustness of the proposed super-efficiency model. Finally, two numerical examples are presented to demonstrate the applicability of the proposed chance-constrained DEA model and sensitivity analysis.

Suggested Citation

  • Madjid Tavana & Rashed Khanjani Shiraz & Adel Hatami-Marbini, 2014. "A new chance-constrained DEA model with birandom input and output data," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 65(12), pages 1824-1839, December.
    • Handle: RePEc:pal:jorsoc:v:65:y:2014:i:12:p:1824-1839
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    Citations

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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. Meiqiang Wang & Yingwen Chen & Zhixiang Zhou, 2020. "A Novel Stochastic Two-Stage DEA Model for Evaluating Industrial Production and Waste Gas Treatment Systems," Sustainability, MDPI, vol. 12(6), pages 1-17, March.
    3. 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.
    4. Mingting Kou & Yi Zhang & Yu Zhang & Kaihua Chen & Jiancheng Guan & Senmao Xia, 2020. "Does gender structure influence R&D efficiency? A regional perspective," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(1), pages 477-501, January.
    5. Toloo, Mehdi & Keshavarz, Esmaeil & Hatami-Marbini, Adel, 2018. "Dual-role factors for imprecise data envelopment analysis," Omega, Elsevier, vol. 77(C), pages 15-31.
    6. Chen, Zhongfei & Matousek, Roman & Wanke, Peter, 2018. "Chinese bank efficiency during the global financial crisis: A combined approach using satisficing DEA and Support Vector Machines☆," The North American Journal of Economics and Finance, Elsevier, vol. 43(C), pages 71-86.
    7. Mohammad Izadikhah & Reza Farzipoor Saen, 2023. "Developing a linear stochastic two-stage data envelopment analysis model for evaluating sustainability of supply chains: a case study in welding industry," Annals of Operations Research, Springer, vol. 322(1), pages 195-215, March.
    8. Rashed Khanjani Shiraz & Madjid Tavana & Hirofumi Fukuyama, 2021. "A joint chance-constrained data envelopment analysis model with random output data," Operational Research, Springer, vol. 21(2), pages 1255-1277, June.
    9. Rashed Khanjani Shiraz & Adel Hatami-Marbini & Ali Emrouznejad & Hirofumi Fukuyama, 2020. "Chance-constrained cost efficiency in data envelopment analysis model with random inputs and outputs," Operational Research, Springer, vol. 20(3), pages 1863-1898, September.
    10. Ali Ebrahimnejad & Madjid Tavana & Seyed Hadi Nasseri & Omid Gholami, 2019. "A New Method for Solving Dual DEA Problems with Fuzzy Stochastic Data," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(01), pages 147-170, January.

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