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Augmented simulation methods for discrete stochastic optimization with recourse
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DOI：10.1007/s10479-020-03836-w.png)
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We develop an augmented simulation approach to solve discrete stochastic optimization problems by converting them into a grand simulation problem in the joint space of random and decision variables. The optimal decision is obtained via the mode of the augmented probability model, using a new multivariate extension of the classic Barker's algorithm. Illustrations on different versions of univariate and multivariate discrete news-vendor problems with exogenous and endogenous uncertainties are detailed. We contrast our method with the Metropolis-Hastings algorithm, the nested sampling-based augmented simulation method, and traditional Monte Carlo simulation-based optimization schemes. The proposed method is shown to be computationally efficient and could serve as another tool to solve discrete stochastic optimization problems with recourse.
Keyword:
Discrete stochastic optimization
Simulation-based optimization
Augmented probability simulation
Barker algorithm
Stochastic programs with recourse
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