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• W2186356962 abstract "Free AccessAboutSectionsView PDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinked InEmail Go to SectionFree Access HomeINFORMS TutORials in Operations ResearchBridging Data and Decisions Clearing the Jungle of Stochastic OptimizationWarren B. PowellWarren B. PowellPublished Online:27 Oct 2014https://doi.org/10.1287/educ.2014.0128Abstract Whereas deterministic optimization enjoys an almost universally accepted canonical form, stochastic optimization is a jungle of competing notational systems and algorithmic strategies. This is especially problematic in the context of sequential (multistage) stochastic optimization problems, which is the focus of our presentation. In this article, we place a variety of competing strategies into a common framework, which makes it easier to see the close relationship between communities such as stochastic programming, (approximate) dynamic programming, simulation, and stochastic search. What have previously been viewed as competing approaches (e.g., simulation versus optimization, stochastic programming versus dynamic programming) can be reduced to four fundamental classes of policies that are evaluated in a simulation-based setting we call the base model. The result is a single coherent framework that encompasses all of these methods, which can often be combined to create powerful hybrid policies to address complex problems. This publication has no references to display. 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 FiguresReferencesRelatedInformationCited byNo Longer in the Dark: Utilizing Imperfect Advance Load Information for Single-Truck OperatorsMehdi Najafi, Hossein Zolfagharinia25 March 2022 | Transportation Science, Vol. 56, No. 6Multi-Period Workload Balancing in Last-Mile Urban DeliveryYang Wang, Lei Zhao, Martin Savelsbergh, Shengnan Wu17 March 2022 | Transportation Science, Vol. 56, No. 5A Discrete Simulation-Based Optimization Algorithm for the Design of Highly Responsive Last-Mile Distribution NetworksAndré Snoeck, Matthias Winkenbach30 November 2021 | Transportation Science, Vol. 56, No. 1Easy Affine Markov Decision ProcessesJie Ning, Matthew J. Sobel25 October 2019 | Operations Research, Vol. 67, No. 6A New Approach to Real-Time Bidding in Online Advertisements: Auto Pricing StrategyShalinda Adikari, Kaushik Dutta21 January 2019 | INFORMS Journal on Computing, Vol. 31, No. 1A Unified Framework for Optimization Under UncertaintyWarren B. Powell4 November 2016 Bridging Data and DecisionsSeptember 2014 Article Information Metrics Information Published Online:October 27, 2014 Copyright © 2014, INFORMSCite asWarren B. Powell (2014) Clearing the Jungle of Stochastic Optimization. INFORMS TutORials in Operations Research null(null):109-137. https://doi.org/10.1287/educ.2014.0128 Keywordsstochastic optimizationstochastic programmingapproximate dynamic programmingreinforcement learningPDF download" @default.
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• W2186356962 date "2014-09-01" @default.
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• W2186356962 title "Clearing the Jungle of Stochastic Optimization" @default.
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• W2186356962 doi "https://doi.org/10.1287/educ.2014.0128" @default.
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