Partially observable multistage stochastic optimization
2020, Operations Research Letters, 48(4), pp.505-512
Bayesian
MultistagePartially observable
Stochastic dual dynamic programming
Stochastic programming
Abstract
We propose a class of partially observable multistage stochastic programs and describe an algorithm for solving this class of problems. We provide a Bayesian update of a belief-state vector, extend the stochastic programming formulation to incorporate the belief state, and characterize saddle-function properties of the corresponding cost-to-go function. Our algorithm is a derivative of the stochastic dual dynamic programming method.