On distributionally robust multiperiod stochastic optimization

Analui, B. & Pflug, G.C. ORCID: https://orcid.org/0000-0001-8215-3550 (2014). On distributionally robust multiperiod stochastic optimization. Computational Management Science 11 (3) 197-220. 10.1007/s10287-014-0213-y.

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This paper considers model uncertainty for multistage stochastic programs. The data and information sructure of the baseline model is a tree, on which the decision problem is defined. We consider "ambiguity neighborhoods" around this tree as alternative models which are close to the baseline model. Closeness is defined in terms of a distance for probability trees, called the nested distance. This distance is appropriate for scenario models of multistage stochastic optimization problems as was demonstrated in Pflug and Pichler (SIAM J Optim 22:1.23, 2012). The ambiguity model is formulated as a minimax problem, where the the optimal decision is to be found, which minimizes the maximal objective function within the ambiguity set. We give a setup for studying saddle point properties of the minimax problem. Moreover, we present solution algorithms for finding the minimax decisions at least symptotically. As an example, we consider a multiperiod stochastic production/inventory control problem with weekly ordering. The stochastic scenario process is given by the random demands for two products. We determine the minimax solution and identify the worst trees within the ambiguity set. It turns out that the probability weights of the worst case trees are concentrated on few very bad scenarios.

Item Type: Article
Uncontrolled Keywords: multistage stochastic optimization; distributional robustness; model ambiguity; nested distance; inventory management
Research Programs: Risk & Resilience (RISK)
Risk, Policy and Vulnerability (RPV)
Bibliographic Reference: Computational Management Science; 11(3):197-220 [July 2014]
Depositing User: IIASA Import
Date Deposited: 15 Jan 2016 08:50
Last Modified: 27 Aug 2021 17:23
URI: https://pure.iiasa.ac.at/10760

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