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Chance constrained programming approaches to congestion in stochastic data envelopment analysis

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  1. Wanke, Peter & Barros, C.P. & Figueiredo, Otávio, 2016. "Efficiency and productive slacks in urban transportation modes: A two-stage SDEA-Beta Regression approach," Utilities Policy, Elsevier, vol. 41(C), pages 31-39.
  2. 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.
  3. 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.
  4. Wei, Quanling & Yan, Hong, 2009. "Weak congestion in output additive data envelopment analysis," Socio-Economic Planning Sciences, Elsevier, vol. 43(1), pages 40-54, March.
  5. Xiao, Helu & Ren, Tiantian & Zhou, Zhongbao & Liu, Wenbin, 2021. "Parameter uncertainty in estimation of portfolio efficiency: Evidence from an interval diversification-consistent DEA approach," Omega, Elsevier, vol. 103(C).
  6. Balak, Sima & Behzadi, Mohammad Hassan & Nazari, Ali, 2021. "Stochastic copula-DEA model based on the dependence structure of stochastic variables: An application to twenty bank branches," Economic Analysis and Policy, Elsevier, vol. 72(C), pages 326-341.
  7. Toshiyuki Sueyoshi & Mika Goto, 2020. "Performance Assessment of Japanese Electric Power Industry: DEA Measurement with Future Impreciseness," Energies, MDPI, vol. 13(2), pages 1-24, January.
  8. 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.
  9. Leila Parhizkar Miyandehi & Alireza Amirteimoori & Sohrab Kordrostami & Mansour Soufi, 2024. "Optimal scale sizes and average-profit efficiency under uncertainty: A chance-constrained DEA approach," PLOS ONE, Public Library of Science, vol. 19(6), pages 1-21, June.
  10. Zhimin Huang & Waiman Cheung & Huiwen Wang, 2006. "Cone dominance and efficiency in DEA," Annals of Operations Research, Springer, vol. 145(1), pages 89-103, July.
  11. Amirteimoori, Alireza & Cezar, Asunur & Zadmirzaei, Majid & Susaeta, Andres, 2024. "Environmental performance evaluation in the forest sector: An extended stochastic data envelopment analysis approach," Socio-Economic Planning Sciences, Elsevier, vol. 94(C).
  12. Pang, Qinghua & Qiu, Man & Zhang, Lina & Chiu, Yung-ho, 2023. "Congestion effects of energy and capital in China's carbon emission reduction: Evidence from provincial levels," Energy, Elsevier, vol. 274(C).
  13. Esfandyar Lashani & Kourosh Aryavash, 2018. "The optimistic–pessimistic revenue distribution in the presence of imprecise data," OPSEARCH, Springer;Operational Research Society of India, vol. 55(2), pages 288-301, June.
  14. Masahiro Inuiguchi & Fumiki Mizoshita, 2012. "Qualitative and quantitative data envelopment analysis with interval data," Annals of Operations Research, Springer, vol. 195(1), pages 189-220, May.
  15. Miguel A. Lejeune & Andrzej Ruszczyński, 2007. "An Efficient Trajectory Method for Probabilistic Production-Inventory-Distribution Problems," Operations Research, INFORMS, vol. 55(2), pages 378-394, April.
  16. Zhu, Y. & Li, Y.P. & Huang, G.H., 2012. "Planning municipal-scale energy systems under functional interval uncertainties," Renewable Energy, Elsevier, vol. 39(1), pages 71-84.
  17. Dianzheng Fu & Tianji Yang & Yize Huang & Yiming Tong, 2022. "Integrated Optimization for Biofuel Management Associated with a Biomass-Penetrated Heating System under Multiple and Compound Uncertainties," Energies, MDPI, vol. 15(15), pages 1-21, July.
  18. Mergoni, Anna & Emrouznejad, Ali & De Witte, Kristof, 2025. "Fifty years of Data Envelopment Analysis," European Journal of Operational Research, Elsevier, vol. 326(3), pages 389-412.
  19. 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.
  20. Khodabakhshi, M., 2009. "Estimating most productive scale size with stochastic data in data envelopment analysis," Economic Modelling, Elsevier, vol. 26(5), pages 968-973, September.
  21. Cui, Yuan & Pan, Hao & Huang, Yi-Di & Yang, Guo-liang, 2024. "How can sociological theories provide legitimacy to eco-efficiency evaluations? Embark on a journey toward understanding," Socio-Economic Planning Sciences, Elsevier, vol. 93(C).
  22. Nahia Mourad & Assem Tharwat, 2019. "Mixed Stochastic Input Oriented Data Envelopment Analysis Model," Working Papers hal-02144705, HAL.
  23. Li, Mo & Guo, Ping, 2015. "A coupled random fuzzy two-stage programming model for crop area optimization—A case study of the middle Heihe River basin, China," Agricultural Water Management, Elsevier, vol. 155(C), pages 53-66.
  24. Vincent Charles & Ioannis E. Tsolas & Tatiana Gherman, 2018. "Satisficing data envelopment analysis: a Bayesian approach for peer mining in the banking sector," Annals of Operations Research, Springer, vol. 269(1), pages 81-102, October.
  25. 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.
  26. Glover, Fred & Sueyoshi, Toshiyuki, 2009. "Contributions of Professor William W. Cooper in Operations Research and Management Science," European Journal of Operational Research, Elsevier, vol. 197(1), pages 1-16, August.
  27. Majid Azadi & T. C. E. Cheng & Reza Kazemi Matin & Reza Farzipoor Saen, 2024. "The COVID-19 pandemic and the performance of healthcare supply chains," Annals of Operations Research, Springer, vol. 335(1), pages 535-562, April.
  28. 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.
  29. O. Olesen, 2006. "Comparing and Combining Two Approaches for Chance Constrained DEA," Journal of Productivity Analysis, Springer, vol. 26(2), pages 103-119, October.
  30. Valentin Zelenyuk, 2019. "Data Envelopment Analysis and Business Analytics: The Big Data Challenges and Some Solutions," CEPA Working Papers Series WP072019, School of Economics, University of Queensland, Australia.
  31. Ewa Chodakowska & Joanicjusz Nazarko, 2020. "Assessing the Performance of Sustainable Development Goals of EU Countries: Hard and Soft Data Integration," Energies, MDPI, vol. 13(13), pages 1-26, July.
  32. 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.
  33. 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.
  34. Yongjun Li & Xiao Shi & Min Yang & Liang Liang, 2017. "Variable selection in data envelopment analysis via Akaike’s information criteria," Annals of Operations Research, Springer, vol. 253(1), pages 453-476, June.
  35. Wang, Ping & An, Qingxian & Liang, Liang, 2025. "Consumer preference estimation based on intertemporal choice data: A chance constrained data envelopment analysis method," European Journal of Operational Research, Elsevier, vol. 325(3), pages 487-499.
  36. Majid Azadi & Mehdi Toloo & Fahimeh Ramezani & Reza Farzipoor Saen & Farookh Khadeer Hussain & Hajar Farnoudkia, 2024. "Evaluating efficiency of cloud service providers in era of digital technologies," Annals of Operations Research, Springer, vol. 342(2), pages 1049-1078, November.
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