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Augmented nested sampling for stochastic programs with recourse and endogenous uncertainty
Author(s) -
Ekin Tahir,
Polson Nicholas G.,
Soyer Refik
Publication year - 2017
Publication title -
naval research logistics (nrl)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.665
H-Index - 68
eISSN - 1520-6750
pISSN - 0894-069X
DOI - 10.1002/nav.21778
Subject(s) - newsvendor model , computer science , mathematical optimization , markov chain monte carlo , stochastic programming , monte carlo method , decision problem , stochastic optimization , markov decision process , markov process , mathematics , supply chain , algorithm , artificial intelligence , bayesian probability , statistics , political science , law
We propose a novel simulation‐based approach for solving two‐stage stochastic programs with recourse and endogenous (decision dependent) uncertainty. The proposed augmented nested sampling approach recasts the stochastic optimization problem as a simulation problem by treating the decision variables as random. The optimal decision is obtained via the mode of the augmented probability model. We illustrate our methodology on a newsvendor problem with stock‐dependent uncertain demand both in single and multi‐item (news‐stand) cases. We provide performance comparisons with Markov chain Monte Carlo and traditional Monte Carlo simulation‐based optimization schemes. Finally, we conclude with directions for future research.