Second Order Stochastic Dominance Constraints in Multi-objective Stochastic Programming Problems

0490685 - ÚTIA 2019 SK eng C - Conference Paper (international conference)
Kaňková, Vlasta
Second Order Stochastic Dominance Constraints in Multi-objective Stochastic Programming Problems.
Quantitative Methods in Economics: Multiple Criteria Decision Making XIX. Bratislava: University of Economics, Bratislava, 2018 - (Reiff, M.; Gežík, P.), s. 165-171. ISBN 978-80-89962-07-5.
[Quantitative Methods in Economics: Multiple Criteria Decision Making XIX. Trenčianské Teplice (SK), 23.05.2018-25.05.2018]
R&D Projects: GA ČR GA18-02739S
Institutional support: RVO:67985556
Keywords : stochastic multi-objective optimization problems * efficient solution * Wasserstein metric and L1 norm * Lipschitz property * second order stochastic dominance constraints * relaxation
OECD category: Applied Economics, Econometrics
http://library.utia.cas.cz/separaty/2018/E/kankova-0490685.pdf

Many economic and financial applications lead to determi-nistic optimization problems depending on a probability measure. These problems can be either static (one stage) or dynamic with finite (multistage) or infinite horizon, single-objective or multi-objective. Constraints sets can be either "deterministic", given by probability constraints, or stochastic dominance constraints. We focus on multi-objective problems and second order stochastic dominance constraints. To this end we employ the former results obtained for stochastic (mostly strongly) convex multi-objective problems and results obtained for one-objective problems with second-order stochastic dominance constraints. The relaxation approach will be included in the case of second order stochastic dominance constraints.
Permanent Link: http://hdl.handle.net/11104/0286787