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Multistage stochastic decision problems: Approximation by recursive structures and ambiguity modeling
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DOI：10.1016/j.ejor.2022.04.002.png)
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Abstract
En 中文
Stochastic multistage decision problems appear in many -if not all -application areas of Operations Re-search. While to define such problems is easy, to solve them is quite difficult, since they are of infinite dimension. Numerical solution can only be found by solving an approximate, easier problem. In this pa-per, we show good approximations can be found, where we emphasize the recursive structure of the involved algorithms and data structures. In a second part, the problem of coping with the model error of approximations is discussed. We present algorithms for finding distributionally robust solutions for the model error problem. We also review some application cases of such situations from the literature.(c) 2022 The Author. Published by Elsevier B.V. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ )
Keywords:
Stochastic programming
Scenario tree generation
Recursive algorithms
Model error
Distributionally robust solutions
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