Simulation Optimization in the New Era of AI

AboutSectionsRequest Access ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinked InEmail Go to Section HomeINFORMS TutORials in Operations ResearchTutorials in Operations Research: Advancing the Frontiers of OR/MS: From Methodologies to Applications Simulation Optimization in the New Era of AIYijie Peng, Chun-Hung Chen, Michael C. FuYijie Peng, Chun-Hung Chen, Michael C. FuPublished Online:13 Oct 2023https://doi.org/10.1287/educ.2023.0264AbstractWe review simulation optimization methods and discuss how these methods underpin modern artificial intelligence (AI) techniques. In particular, we focus on three areas: stochastic gradient estimation, which plays a central role in training neural networks for deep learning and reinforcement learning; simulation sample allocation, which can be used as the node selection policy in Monte Carlo tree search; and variance reduction, which can accelerate training procedures in AI.Funding: This work was supported in part by the U.S. Air Force Office of Scientific Research [Grant FA95502010211], the National Natural Science Foundation of China [Grants 72250065, 72022001, and 71901003], and U.S. National Science Foundation [Grant FAIN 2123683]. Your Access Options Login Options INFORMS Member Login Nonmember Login Purchase Options Save for later Item saved, go to cart Tutorials in OR, TutorialsNew $20.00 Add to cart Tutorials in OR, TutorialsNew Checkout Other Options Token Access Insert token number Claim access using a token Restore guest access Applies for purchases made as a guest Previous Back to Top Next FiguresReferencesRelatedInformation Tutorials in Operations Research: Advancing the Frontiers of OR/MS: From Methodologies to ApplicationsOctober 2023 Article Information Metrics Information Published Online:October 13, 2023 Copyright © 2023, INFORMSCite asYijie Peng, Chun-Hung Chen, Michael C. Fu (2023) Simulation Optimization in the New Era of AI. INFORMS TutORials in Operations Research null(null):82-108. https://doi.org/10.1287/educ.2023.0264 Keywordssimulation optimizationgradient estimationsimulation sample allocationvariance reductiondeep learningreinforcement learningMonte Carlo tree searchPDF download