TY - JOUR
T1 - Partially observable multistage stochastic programming
AU - Dowson, Oscar
AU - Morton, David P.
AU - Pagnoncelli, Bernardo K.
N1 - Funding Information:
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.
Publisher Copyright:
© 2020 Elsevier B.V.
PY - 2020/7
Y1 - 2020/7
N2 - 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.
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.
KW - Bayesian
KW - Multistage
KW - Partially observable
KW - Stochastic dual dynamic programming
KW - Stochastic programming
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U2 - 10.1016/j.orl.2020.06.005
DO - 10.1016/j.orl.2020.06.005
M3 - Article
AN - SCOPUS:85086720789
VL - 48
SP - 505
EP - 512
JO - Operations Research Letters
JF - Operations Research Letters
SN - 0167-6377
IS - 4
ER -