Convexity of chance constraints with independent random variables
Computational Optimization and Applications
An integer programming approach for linear programs with probabilistic constraints
Mathematical Programming: Series A and B
On joint probabilistic constraints with Gaussian coefficient matrix
Operations Research Letters
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We study stochastic linear programs with joint chance constraints, where the random matrix is a special triangular matrix and the random data are assumed to be normally distributed. The problem can be approximated by another stochastic program, whose optimal value is an upper bound of the original problem. The latter stochastic program can be approximated by two second-order cone programming (SOCP) problems [5]. Furthermore, in some cases, the optimal values of the two SOCPs problems provide a lower bound and an upper bound of the approximated stochastic program respectively. Finally, numerical examples with probabilistic lot-sizing problems are given to illustrate the effectiveness of the two approximations.