Partially observable multistage stochastic optimization

Dowson, Oscar; Morton, David; Pagnoncelli, B.K.

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.

Más información

Título de la Revista: OPERATIONS RESEARCH LETTERS
Editorial: ELSEVIER SCIENCE BV
Fecha de publicación: 2020