AUTORES

ANDY BRYAN PHILPOTT

ERLON C. FINARDI

FEV/2013

REVISTA: OPERATIONS RESEARCH

EDIÇÃO: 61 / 4

PÁGINAS: 957-970

We consider a class of multistage stochastic linear programs in which at each stage a coherent risk measure of future costs is to be minimized. A general computational approach based on dynamic programming is derived that can be shown to converge to an optimal policy. By computing an inner approximation to future cost functions, we can evaluate an upper bound on the cost of an optimal policy, and an outer approximation delivers a lower bound. The approach we describe is particularly useful in sampling-based algorithms, and a numerical example is provided to show the efficacy of the methodology when used in conjunction with stochastic dual dynamic programming.