Partially observable multistage stochastic programming

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-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 languageEnglish (US)
Pages (from-to)505-512
Number of pages8
JournalOperations Research Letters
Volume48
Issue number4
DOIs
StatePublished - 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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