Multistage stochastic decision problems: Approximation by recursive structures and ambiguity modeling
研究了多阶段随机决策问题的近似求解方法，强调算法和数据结构的递归结构，并讨论了处理近似模型误差的分布鲁棒算法。
Stochastic multistage decision problems appear in many - if not all - application areas of Operations Research. 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 paper, 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.