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Technical Note—A Class of Nonlinear Chance-Constrained Programming Models with Joint Constraints

Author

Listed:
  • R. Jagannathan

    (Columbia University, New York, New York)

  • M. R. Rao

    (University of Rochester, Rochester, New York)

Abstract

Miller and Wagner [ Opns. Res. 13, 930–945 (1965)] define joint chance-constrained programming by specifying a set of constants that are joint probability measures of the extent to which constraint violations are permitted. For the special case of a random right-hand-side vector whose elements are independent random variables, they show that an equivalent deterministic concave program exists. The purpose of this paper is to generalize this result to a class of nonlinear chance-constrained programming models with joint constraints.

Suggested Citation

  • R. Jagannathan & M. R. Rao, 1973. "Technical Note—A Class of Nonlinear Chance-Constrained Programming Models with Joint Constraints," Operations Research, INFORMS, vol. 21(1), pages 360-364, February.
    • Handle: RePEc:inm:oropre:v:21:y:1973:i:1:p:360-364
    DOI: 10.1287/opre.21.1.360
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    Cited by:

    1. Yong Zeng & Yanpeng Cai & Guohe Huang & Jing Dai, 2011. "A Review on Optimization Modeling of Energy Systems Planning and GHG Emission Mitigation under Uncertainty," Energies, MDPI, vol. 4(10), pages 1-33, October.

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