Bound-Based Decision Rules in Multistage Stochastic Programming
Journal
Kybernetika
ISSN
0023-5954
Type
journal article
Date Issued
2008-05-01
Author(s)
Kuhn, Daniel
;
Parpas, Panos
;
Rustem, Berç
Abstract
We study bounding approximations for a multistage stochastic program with expected value constraints. Two simpler approximate stochastic programs, which provide upper and lower bounds on the original problem, are obtained by replacing the original stochastic data process by finitely supported approximate processes. We model the original and approximate processes as dependent random vectors on a joint probability space. This probabilistic coupling allows us to transform the optimal solution of the upper bounding problem to a near-optimal decision rule for the original problem. Unlike the scenario tree based solutions of the bounding problems, the resulting decision rule is implementable in all decision stages, i. e., there is no need for dynamic reoptimization during the planning period. Our approach is illustrated with a mean-risk portfolio optimization model.
Language
English
Keywords
stochastic programming
bounds
decision rules
expected value constraints
portfolio optimization
HSG Classification
contribution to scientific community
Refereed
Yes
Publisher
Institute of Information Theory and Automation of the Academy of Sciences of the Czech Republic
Publisher place
Prague
Volume
44
Number
2
Start page
134
End page
150
Pages
17
Subject(s)
Division(s)
Eprints ID
60630