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Chance-Constrained Programming with Joint Constraints

Author

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  • R. Jagannathan

    (Columbia University, New York, New York)

Abstract

Miller and Wagner have shown that a deterministic equivalent of a joint chance-constrained programming model with independent random right-hand-side elements is a concave programming problem. This paper obtains similar equivalents for chance-constrained programming models with coefficient matrices whose elements are normally distributed and with dependent random right-hand-side elements.

Suggested Citation

  • R. Jagannathan, 1974. "Chance-Constrained Programming with Joint Constraints," Operations Research, INFORMS, vol. 22(2), pages 358-372, April.
    • Handle: RePEc:inm:oropre:v:22:y:1974:i:2:p:358-372
    DOI: 10.1287/opre.22.2.358
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    Cited by:

    1. Aigner, Kevin-Martin & Clarner, Jan-Patrick & Liers, Frauke & Martin, Alexander, 2022. "Robust approximation of chance constrained DC optimal power flow under decision-dependent uncertainty," European Journal of Operational Research, Elsevier, vol. 301(1), pages 318-333.
    2. Zhiping Chen & Shen Peng & Jia Liu, 2018. "Data-Driven Robust Chance Constrained Problems: A Mixture Model Approach," Journal of Optimization Theory and Applications, Springer, vol. 179(3), pages 1065-1085, December.
    3. Raffaello Seri & Christine Choirat, 2013. "Scenario Approximation of Robust and Chance-Constrained Programs," Journal of Optimization Theory and Applications, Springer, vol. 158(2), pages 590-614, August.
    4. Adane Abebaw Gessesse & Rajashree Mishra & Mitali Madhumita Acharya & Kedar Nath Das, 2020. "Genetic algorithm based fuzzy programming approach for multi-objective linear fractional stochastic transportation problem involving four-parameter Burr distribution," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 11(1), pages 93-109, February.
    5. Marla, Lavanya & Rikun, Alexander & Stauffer, Gautier & Pratsini, Eleni, 2020. "Robust modeling and planning: Insights from three industrial applications," Operations Research Perspectives, Elsevier, vol. 7(C).
    6. Cooper, W. W. & Hemphill, H. & Huang, Z. & Li, S. & Lelas, V. & Sullivan, D. W., 1997. "Survey of mathematical programming models in air pollution management," European Journal of Operational Research, Elsevier, vol. 96(1), pages 1-35, January.
    7. Zhiping Chen & Shen Peng & Abdel Lisser, 2020. "A sparse chance constrained portfolio selection model with multiple constraints," Journal of Global Optimization, Springer, vol. 77(4), pages 825-852, August.
    8. D. K. Mohanty & Avik Pradhan & M. P. Biswal, 2020. "Chance constrained programming with some non-normal continuous random variables," OPSEARCH, Springer;Operational Research Society of India, vol. 57(4), pages 1281-1298, December.
    9. Michel Minoux & Riadh Zorgati, 2019. "Sharp upper and lower bounds for maximum likelihood solutions to random Gaussian bilateral inequality systems," Journal of Global Optimization, Springer, vol. 75(3), pages 735-766, November.
    10. Willis, David B. & Whittlesey, Norman K., 1998. "The Effect Of Stochastic Irrigation Demands And Surface Water Supplies On On-Farm Water Management," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 23(1), pages 1-19, July.
    11. Rashed Khanjani-Shiraz & Salman Khodayifar & Panos M. Pardalos, 2021. "Copula theory approach to stochastic geometric programming," Journal of Global Optimization, Springer, vol. 81(2), pages 435-468, October.

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