Abstract
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.
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Cheng, J., Gicquel, C., Lisser, A. (2012). A Second-Order Cone Programming Approximation to Joint Chance-Constrained Linear Programs. In: Mahjoub, A.R., Markakis, V., Milis, I., Paschos, V.T. (eds) Combinatorial Optimization. ISCO 2012. Lecture Notes in Computer Science, vol 7422. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32147-4_8
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DOI: https://doi.org/10.1007/978-3-642-32147-4_8
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