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Lukas Adam (adamutia.cas.cz) Abstract: We consider stochastic programs with joint chance constraints with discrete random distribution. We reformulate the problem by adding auxiliary variables. Since the resulting problem has a nonregular feasible set, we regularize it by increasing the feasible set. We solve the regularized problem by iteratively solving a master problem while adding Benders' cuts in a slave problem. Since the number of variables of the slave problem equals to the number of scenarios, we express its solution in a closed form. We show convergence properties of the solutions. On a gas network design problem, we perform a numerical study by increasing the number of scenarios and compare our solution with a solution obtained by solving the same problem with the continuous distribution. Keywords: Stochastic programming; Chance constrained programming; Optimality conditions; Regularization; Benders decomposition; Gas networks Category 1: Stochastic Programming Citation: L. Adam, M. Branda, H. Heitsch, R. Henrion: Solving joint chance constrained problems using regularization and Benders' decomposition. Submitted, 2018. Download: [PDF] Entry Submitted: 01/06/2018 Modify/Update this entry  
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