| bibtype |
J -
Journal Article
|
| ARLID |
0587649 |
| utime |
20240729113336.6 |
| mtime |
20240716235959.9 |
| SCOPUS |
85186174671 |
| WOS |
001175189200001 |
| DOI |
10.1007/s11846-024-00733-5 |
| title
(primary) (eng) |
Approximation of multistage stochastic programming problems by smoothed quantization |
| specification |
| page_count |
36 s. |
| media_type |
P |
|
| serial |
| ARLID |
cav_un_epca*0378519 |
| ISSN |
1863-6683 |
| title
|
Review of Managerial Science |
| volume_id |
18 |
| volume |
1 (2024) |
| page_num |
2079-2114 |
| publisher |
|
|
| keyword |
Multistage stochastic programming |
| keyword |
Approximation |
| keyword |
Markov dependence |
| keyword |
SDDP |
| author
(primary) |
| ARLID |
cav_un_auth*0101206 |
| name1 |
Šmíd |
| name2 |
Martin |
| institution |
UTIA-B |
| full_dept (cz) |
Ekonometrie |
| full_dept (eng) |
Department of Econometrics |
| department (cz) |
E |
| department (eng) |
E |
| full_dept |
Department of Econometrics |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0363894 |
| name1 |
Kozmík |
| name2 |
Václav |
| institution |
UTIA-B |
| full_dept (cz) |
Ekonometrie |
| full_dept |
Department of Econometrics |
| department (cz) |
E |
| department |
E |
| country |
CZ |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| source |
|
| source |
|
| cas_special |
| project |
| project_id |
GA21-07494S |
| agency |
GA ČR |
| country |
CZ |
| ARLID |
cav_un_auth*0430801 |
|
| abstract
(eng) |
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. |
| result_subspec |
WOS |
| RIV |
BB |
| FORD0 |
10000 |
| FORD1 |
10100 |
| FORD2 |
10103 |
| reportyear |
2025 |
| num_of_auth |
2 |
| inst_support |
RVO:67985556 |
| permalink |
https://hdl.handle.net/11104/0355033 |
| confidential |
S |
| mrcbC91 |
C |
| mrcbT16-e |
MANAGEMENT |
| mrcbT16-j |
1.161 |
| mrcbT16-D |
Q2 |
| arlyear |
2024 |
| mrcbU14 |
85186174671 SCOPUS |
| mrcbU24 |
PUBMED |
| mrcbU34 |
001175189200001 WOS |
| mrcbU63 |
cav_un_epca*0378519 Review of Managerial Science 18 1 2024 2079 2114 1863-6683 1863-6691 Springer |
|