Math Program. 2026;217(1-2):43-78. doi: 10.1007/s10107-026-02368-8. Epub 2026 Jun 5.
ABSTRACT
We study a class of multi-stage stochastic programs, which incorporate modeling features from Markov decision processes (MDPs). This class includes structured MDPs with continuous action and state spaces. We extend policy graphs to include decision-dependent uncertainty for one-step transition probabilities as well as a limited form of statistical learning. We focus on the expressiveness of our modeling approach, illustrating ideas with a series of examples of increasing complexity. As a solution method, we develop new variants of stochastic dual dynamic programming, including approximations to handle non-convexities.
PMID:42602018 | PMC:PMC13473183 | DOI:10.1007/s10107-026-02368-8
