Augmented simulation methods for discrete stochastic optimization with recourse

Tahir Ekin (), Stephen Walker and Paul Damien
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Tahir Ekin: Texas State University
Stephen Walker: University of Texas in Austin
Paul Damien: University of Texas in Austin

Annals of Operations Research, 2023, vol. 320, issue 2, No 10, 793 pages

Abstract: Abstract 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.

Keywords: Discrete stochastic optimization; Simulation-based optimization; Augmented probability simulation; Barker algorithm; Stochastic programs with recourse (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10479-020-03836-w

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