0501589 20240103221607.020190215235959.9 85056150669 000563054500006 10.1007/s10479-018-3091-9 27 s. P cav_un_epca*02508070254-53302922 (2020)683-709Springer Stochastic programming Chance constrained programming Optimality conditions Regularization Benders' decomposition Gas networks cav_un_auth*0309054 Adam Lukáš UTIA-B Matematická teorie rozhodování Department of Decision Making Theory MTR MTR Department of Decision Making Theory CZ Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0280972 Branda Martin UTIA-B Matematická teorie rozhodování Department of Decision Making Theory MTR MTR Department of Decision Making Theory CZ Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0372396 Heitsch H. DE cav_un_auth*0015558 Henrion R. DE http://library.utia.cas.cz/separaty/2019/MTR/adam-0501589.pdf https://link.springer.com/article/10.1007/s10479-018-3091-9 GA18-04145S GA ČR CZ cav_un_auth*0373104 GA18-05631S GA ČR CZ cav_un_auth*0373105 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. WOS BA 10000 10100 10101 2021 4 RVO:67985556 http://hdl.handle.net/11104/0294165 cav_un_auth*0372394 Southern University of Science and Technology CN cav_un_auth*0295067 Univerzita Karlova v Praze UK CZ cav_un_auth*0305285 Weierstraß-Institut für Angewandte Analysis und Stochastik DE S 2 Article Operations Research Management Science C OPERATIONSRESEARCH&MANAGEMENTSCIENCE 4.161 1.247 5.4 0.01217 0.835 12669 132 1.068 43.88 3.33 3295 Q1 3.816 651 Q1 39.133 75.6 Q3 Q2 0.98 Q2 75.595 2020 85056150669 SCOPUS PUBMED 000563054500006 WOS cav_un_epca*0250807 Annals of Operations Research 0254-5330 1572-9338 Roč. 292 č. 2 2020 683 709 Springer