0450553 20240103211213.220160215235959.9 84941338155 000374450100005 10.1007/s10100-015-0414-7 25 s. P cav_un_epca*02930281435-246X242 (2016)309-333 Stochasticmultiobjective optimization problem Efficient solution Wasserstein metric and L_1 norm Stability and empirical estimates cav_un_auth*0101122 Ekonometrie Department of Econometrics E E Department of Econometrics 100 Kaňková Vlasta UTIA-B Ústav teorie informace a automatizace AV ČR, v. v. i. http://library.utia.cas.cz/separaty/2015/E/kankova-0450553.pdf cav_un_auth*0292652 GA13-14445S GA ČR Many economic and financial applications lead (from the mathematical point of view) to deterministic optimization problems depending on a probability measure. These problems can be static (one stage), dynamic with finite (multistage) or infinite horizon, single objective or multiobjective. We focus on one-stage case in multiobjective setting. Evidently, well known results from the deterministic optimization theory can be employed in the case when the "underlying" probability measure is completely known. The assumption of a complete knowledge of the probability measure is fulfilled very seldom. Consequently, we have mostly to analyze the mathematical models on the data base to obtain a stochastic estimate of the corresponding "theoretical" characteristics. However, the investigation of these estimates has been done mostly in one-objective case. In this paper we focus on the investigation of the relationship between "characteristics" obtained on the base of complete knowledge of the probability measure and estimates obtained on the (above mentioned) data base, mostly in the multiobjective case. BB 2017 1 RVO:67985556 http://hdl.handle.net/11104/0252048 S 3+4 Article Operations Research Management Science OPERATIONSRESEARCH&MANAGEMENTSCIENCE 0.904 0.327 5.6 0.00096 0.305 398 0.531 Q3 0.625 49 Q4 7.294 15.1 Q4 Q3 15.06 2016 84941338155 SCOPUS 000374450100005 WOS cav_un_epca*0293028 Central European Journal of Operations Research 1435-246X 1613-9178 Roč. 24 č. 2 2016 309 333