Partially observable multistage stochastic programming

Oscar Dowson; David P. Morton; Bernardo K. Pagnoncelli

doi:10.1016/j.orl.2020.06.005

Partially observable multistage stochastic programming

Oscar Dowson^*, David P. Morton, Bernardo K. Pagnoncelli

^*Corresponding author for this work

Industrial Engineering and Management Sciences

Research output: Contribution to journal › Article › peer-review

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.

Original language	English (US)
Pages (from-to)	505-512
Number of pages	8
Journal	Operations Research Letters
Volume	48
Issue number	4
DOIs	https://doi.org/10.1016/j.orl.2020.06.005
State	Published - Jul 2020

Keywords

Bayesian
Multistage
Partially observable
Stochastic dual dynamic programming
Stochastic programming

ASJC Scopus subject areas

Software
Management Science and Operations Research
Industrial and Manufacturing Engineering
Applied Mathematics

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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.",

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KW - Partially observable

KW - Stochastic dual dynamic programming

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