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

7 Scopus citations

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

Funding

This article was developed, in part, based upon funding from the Alliance for Sustainable Energy, LLC, Managing and Operating Contractor, USA for the National Renewable Energy Laboratory for the U.S. Department of Energy. The authors thank three referees whose comments improved the paper.

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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10.1016/j.orl.2020.06.005

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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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AU - Morton, David P.

AU - Pagnoncelli, Bernardo K.

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AB - 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

KW - Stochastic programming

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Partially observable multistage stochastic programming

Abstract

Funding

Keywords

ASJC Scopus subject areas

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