research-article

Global-local Metamodel-assisted Stochastic Programming via Simulation

Authors:

Hua ZhengAuthors Info & Claims

ACM Transactions on Modeling and Computer Simulation (TOMACS), Volume 31, Issue 1

Article No.: 2, Pages 1 - 34

https://doi.org/10.1145/3411080

Published: 31 December 2020 Publication History

Abstract

To integrate strategic, tactical, and operational decisions, stochastic programming has been widely used to guide dynamic decision-making. In this article, we consider complex systems and introduce the global-local metamodel-assisted stochastic programming via simulation that can efficiently employ the simulation resource to iteratively solve for the optimal first- and second-stage decisions. Specifically, at each visited first-stage decision, we develop a local metamodel to simultaneously solve a set of scenario-based second-stage optimization problems, which also allows us to estimate the optimality gap. Then, we construct a global metamodel accounting for the errors induced by: (1) using a finite number of scenarios to approximate the expected future cost occurring in the planning horizon, (2) second-stage optimality gap, and (3) finite visited first-stage decisions. Assisted by the global-local metamodel, we propose a new simulation optimization approach that can efficiently and iteratively search for the optimal first- and second-stage decisions. Our framework can guarantee the convergence of optimal solution for the discrete two-stage optimization with unknown objective, and the empirical study indicates that it achieves substantial efficiency and accuracy.

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Cited By

Wang T(2024)Ranking and Selection with Two-Stage DecisionSSRN Electronic Journal10.2139/ssrn.4786970Online publication date: 2024
https://doi.org/10.2139/ssrn.4786970
do Amaral JMontevechi Jde Carvalho Miranda Rdos Santos C(2024)Adaptive metamodeling simulation optimization: Insights, challenges, and perspectivesApplied Soft Computing10.1016/j.asoc.2024.112067165(112067)Online publication date: Nov-2024
https://doi.org/10.1016/j.asoc.2024.112067
Wang TXu JHu JChen C(2023)Efficient estimation of a risk measure requiring two-stage simulation optimizationEuropean Journal of Operational Research10.1016/j.ejor.2022.06.028305:3(1355-1365)Online publication date: Mar-2023
https://doi.org/10.1016/j.ejor.2022.06.028

Index Terms

Global-local Metamodel-assisted Stochastic Programming via Simulation
1. Computing methodologies
  1. Artificial intelligence
    1. Planning and scheduling
      1. Planning under uncertainty

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cover image ACM Transactions on Modeling and Computer Simulation

ACM Transactions on Modeling and Computer Simulation Volume 31, Issue 1

January 2021

144 pages

ISSN:1049-3301

EISSN:1558-1195

DOI:10.1145/3446631

Editor:
Francesco Quaglia
University of Rome Tor Vergata, Italy

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Copyright © 2020 ACM.

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Publication History

Published: 31 December 2020

Accepted: 01 July 2020

Revised: 01 May 2020

Received: 01 August 2018

Published in TOMACS Volume 31, Issue 1

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Cited By

Wang T(2024)Ranking and Selection with Two-Stage DecisionSSRN Electronic Journal10.2139/ssrn.4786970Online publication date: 2024
https://doi.org/10.2139/ssrn.4786970
do Amaral JMontevechi Jde Carvalho Miranda Rdos Santos C(2024)Adaptive metamodeling simulation optimization: Insights, challenges, and perspectivesApplied Soft Computing10.1016/j.asoc.2024.112067165(112067)Online publication date: Nov-2024
https://doi.org/10.1016/j.asoc.2024.112067
Wang TXu JHu JChen C(2023)Efficient estimation of a risk measure requiring two-stage simulation optimizationEuropean Journal of Operational Research10.1016/j.ejor.2022.06.028305:3(1355-1365)Online publication date: Mar-2023
https://doi.org/10.1016/j.ejor.2022.06.028

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