0587649 20240729113336.620240716235959.9 85186174671 001175189200001 10.1007/s11846-024-00733-5 36 s. P cav_un_epca*03785191863-6683181 (2024)2079-2114Springer Multistage stochastic programming Approximation Markov dependence SDDP cav_un_auth*0101206 Šmíd Martin UTIA-B Ekonometrie Department of Econometrics E E Department of Econometrics Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0363894 Kozmík Václav UTIA-B Ekonometrie Department of Econometrics E E CZ Ústav teorie informace a automatizace AV ČR, v. v. i. https://library.utia.cas.cz/separaty/2024/E/smid-0587649.pdf https://link.springer.com/content/pdf/10.1007/s11846-024-00733-5.pdf GA21-07494S GA ČR CZ cav_un_auth*0430801 We present an approximation technique for solving multistage stochastic programming problems with an underlying Markov stochastic process. This process is approximated by a discrete skeleton process, which is consequently smoothed down by means of the original unconditional distribution. Approximated in this way, the problem is solvable by means of Markov Stochastic Dual Dynamic Programming. We state an upper bound for the nested distance between the exact process and its approximation and discuss its convergence in the one-dimensional case. We further propose an adjustment of the approximation, which guarantees that the approximate problem is bounded. Finally, we apply our technique to a reallife production-emission trading problem and demonstrate the performance of its approximation given the “true” distribution of the random parameters. WOS BB 10000 10100 10103 2025 2 RVO:67985556 https://hdl.handle.net/11104/0355033 S C MANAGEMENT 8.1 2.2 3.2 0.0045 1.269 4697 63 2.316 102.43 13.48 3285 Q1 8.200 103 Q1 96.3 1.58 Q1 96.3 2024 85186174671 SCOPUS PUBMED 001175189200001 WOS cav_un_epca*0378519 Review of Managerial Science 18 1 2024 2079 2114 1863-6683 1863-6691 Springer