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Augmented simulation methods for discrete stochastic optimization with recourse

Author

Listed:
  • Tahir Ekin

    (Texas State University)

  • Stephen Walker

    (University of Texas in Austin)

  • Paul Damien

    (University of Texas in Austin)

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.

Suggested Citation

  • Tahir Ekin & Stephen Walker & Paul Damien, 2023. "Augmented simulation methods for discrete stochastic optimization with recourse," Annals of Operations Research, Springer, vol. 320(2), pages 771-793, January.
    • Handle: RePEc:spr:annopr:v:320:y:2023:i:2:d:10.1007_s10479-020-03836-w
    DOI: 10.1007/s10479-020-03836-w
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