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Bibliografie

Journal Article

Solving joint chance constrained problems using regularization and Benders’ decomposition

Adam Lukáš, Branda Martin, Heitsch H., Henrion R.

: Annals of Operations Research vol.292, 2 (2020), p. 683-709

: GA18-04145S, GA ČR, GA18-05631S, GA ČR

: Stochastic programming, Chance constrained programming, Optimality conditions, Regularization, Benders' decomposition, Gas networks

: 10.1007/s10479-018-3091-9

: http://library.utia.cas.cz/separaty/2019/MTR/adam-0501589.pdf

: https://link.springer.com/article/10.1007/s10479-018-3091-9

(eng): 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 non-regular 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 from 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.

: BA

: 10101