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Probabilistically constrained models for efficiency and dominance in DEA

Author

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  • Bruni, M.E.
  • Conforti, D.
  • Beraldi, P.
  • Tundis, E.

Abstract

This paper proposes a stochastic model for data envelopment analysis (DEA), based on the theory of joint probabilistic constraints, which can be used with general multivariate distribution functions. The key assumption is that the random variables representative of the uncertain data follow a discrete distribution or that a discrete approximation of continuous distribution is available. Under this assumption, mixed integer linear models are formulated to tackle, rather originally, dependencies among DMUs inputs, outputs and inputs-outputs through the theory of joint probabilistic constraints. The features of the model are illustrated through an application for the performance evaluation of screening units.

Suggested Citation

  • Bruni, M.E. & Conforti, D. & Beraldi, P. & Tundis, E., 2009. "Probabilistically constrained models for efficiency and dominance in DEA," International Journal of Production Economics, Elsevier, vol. 117(1), pages 219-228, January.
    • Handle: RePEc:eee:proeco:v:117:y:2009:i:1:p:219-228
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    References listed on IDEAS

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    7. Prakashan Veettil & Stijn Speelman & Guido Huylenbroeck, 2013. "Estimating the Impact of Water Pricing on Water Use Efficiency in Semi-arid Cropping System: An Application of Probabilistically Constrained Nonparametric Efficiency Analysis," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(1), pages 55-73, January.
    8. Khodadadipour, M. & Hadi-Vencheh, A. & Behzadi, M.H. & Rostamy-malkhalifeh, M., 2021. "Undesirable factors in stochastic DEA cross-efficiency evaluation: An application to thermal power plant energy efficiency," Economic Analysis and Policy, Elsevier, vol. 69(C), pages 613-628.
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    13. Udhayakumar, A. & Charles, V. & Kumar, Mukesh, 2011. "Stochastic simulation based genetic algorithm for chance constrained data envelopment analysis problems," Omega, Elsevier, vol. 39(4), pages 387-397, August.
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    15. Wei, Guiwu & Chen, Jian & Wang, Jiamin, 2014. "Stochastic efficiency analysis with a reliability consideration," Omega, Elsevier, vol. 48(C), pages 1-9.
    16. 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.
    17. Quirós Romero, Cipriano & Rodríguez Rodríguez, Diego, 2010. "E-commerce and efficiency at the firm level," International Journal of Production Economics, Elsevier, vol. 126(2), pages 299-305, August.
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    19. Gianpaolo Iazzolino & Maria Elena Bruni & Patrizia Beraldi, 2013. "Using DEA and financial ratings for credit risk evaluation: an empirical analysis," Applied Economics Letters, Taylor & Francis Journals, vol. 20(14), pages 1310-1317, September.
    20. 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.
    21. 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.
    22. Davtalab-Olyaie, Mostafa & Asgharian, Masoud & Nia, Vahid Partovi, 2019. "Stochastic ranking and dominance in DEA," International Journal of Production Economics, Elsevier, vol. 214(C), pages 125-138.

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