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Input–Output Uncertainty Comparisons for Discrete Optimization via Simulation

Author

Listed:
  • Eunhye Song

    (Department of Industrial and Manufacturing Engineering, The Pennsylvania State University, University Park, Pennsylvania 16802;)

  • Barry L. Nelson

    (Department of Industrial Engineering and Management Sciences, Northwestern University, Evanston, Illinois 60208)

Abstract

When input distributions to a simulation model are estimated from real-world data, they naturally have estimation error causing input uncertainty in the simulation output. If an optimization via simulation (OvS) method is applied that treats the input distributions as “correct,” then there is a risk of making a suboptimal decision for the real world, which we call input model risk . This paper addresses a discrete OvS (DOvS) problem of selecting the real-world optimal from among a finite number of systems when all of them share the same input distributions estimated from common input data. Because input uncertainty cannot be reduced without collecting additional real-world data—which may be expensive or impossible—a DOvS procedure should reflect the limited resolution provided by the simulation model in distinguishing the real-world optimal solution from the others. In light of this, our input–output uncertainty comparisons (IOU-C) procedure focuses on comparisons rather than selection : it provides simultaneous confidence intervals for the difference between each system’s real-world mean and the best mean of the rest with any desired probability, while accounting for both stochastic and input uncertainty. To make the resolution as high as possible (intervals as short as possible) we exploit the common input data effect to reduce uncertainty in the estimated differences. Under mild conditions we prove that the IOU-C procedure provides the desired statistical guarantee asymptotically as the real-world sample size and simulation effort increase, but it is designed to be effective in finite samples.

Suggested Citation

  • Eunhye Song & Barry L. Nelson, 2019. "Input–Output Uncertainty Comparisons for Discrete Optimization via Simulation," Operations Research, INFORMS, vol. 67(2), pages 562-576, March.
  • Handle: RePEc:inm:oropre:v:67:y:2019:i:2:p:562-576
    DOI: 10.1287/opre.2018.1796
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    References listed on IDEAS

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    1. Aharon Ben-Tal & Dick den Hertog & Anja De Waegenaere & Bertrand Melenberg & Gijs Rennen, 2013. "Robust Solutions of Optimization Problems Affected by Uncertain Probabilities," Management Science, INFORMS, vol. 59(2), pages 341-357, April.
    2. Erick Delage & Yinyu Ye, 2010. "Distributionally Robust Optimization Under Moment Uncertainty with Application to Data-Driven Problems," Operations Research, INFORMS, vol. 58(3), pages 595-612, June.
    3. Barry L. Nelson & Frank J. Matejcik, 1995. "Using Common Random Numbers for Indifference-Zone Selection and Multiple Comparisons in Simulation," Management Science, INFORMS, vol. 41(12), pages 1935-1945, December.
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    Cited by:

    1. Amogh Bhosekar & Sandra Ekşioğlu & Tuğçe Işık & Robert Allen, 2023. "A discrete event simulation model for coordinating inventory management and material handling in hospitals," Annals of Operations Research, Springer, vol. 320(2), pages 603-630, January.

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