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Solving joint chance constrained problems using regularization and Benders’ decomposition
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...
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| Published in: | Annals of operations research 2020-09, Vol.292 (2), p.683-709 |
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| Main Authors: | , , , |
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| Language: | English |
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| cited_by | cdi_FETCH-LOGICAL-c420t-5597dc6979bed7b71aee4194a11a2547859bd276aad521e5f477f754104f3f0e3 |
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| cites | cdi_FETCH-LOGICAL-c420t-5597dc6979bed7b71aee4194a11a2547859bd276aad521e5f477f754104f3f0e3 |
| container_end_page | 709 |
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| container_title | Annals of operations research |
| container_volume | 292 |
| creator | Adam, Lukáš Branda, Martin Heitsch, Holger Henrion, René |
| description | 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. |
| doi_str_mv | 10.1007/s10479-018-3091-9 |
| format | article |
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| subjects | Benders decomposition Business and Management Combinatorics Decomposition method Learning models (Stochastic processes) Operations research Operations Research/Decision Theory Regularization S.I.: Stochastic Optimization:Theory&Applications in Memory of M.Bertocchi Stochastic models Theory of Computation Variables |
| title | Solving joint chance constrained problems using regularization and Benders’ decomposition |
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