The Elements of Joint Learning and Optimization in Operations Management / edited by Xi Chen, Stefanus Jasin, Cong Shi

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Resource type: Ressourcentyp: Buch (Online)Book (Online)Language: English Series: Springer Series in Supply Chain Management ; 18 | Springer eBook CollectionPublisher: Cham : Springer International Publishing, 2022Publisher: Cham : Imprint: Springer, 2022Description: 1 Online-Ressource (XIII, 444 p. 21 illus., 10 illus. in color.)ISBN:

9783031019265

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Aufsatzsammlung

Additional physical formats: 9783031019258 | 9783031019272 | 9783031019289 | Erscheint auch als: 9783031019258 Druck-Ausgabe | Erscheint auch als: 9783031019272 Druck-Ausgabe | Erscheint auch als: 9783031019289 Druck-AusgabeDOI: DOI: 10.1007/978-3-031-01926-5Online resources:

Summary: Part 1: Generic Tools -- Chapter 1: The Stochastic Multi-armed Bandit Problem -- Chapter 2: Reinforcement Learning -- Chapter 3: Optimal Learning and Optimal Design -- Part 2: Price Optimization -- Chapter 4: Dynamic Pricing with Demand Learning: Emerging Topics and State of the Art -- Chapter 5: Learning and Pricing with Inventory Constraints -- Chapter 6: Dynamic Pricing and Demand Learning in Nonstationary Environments -- Chapter 7: Pricing with High-Dimensional Data -- Part 3: Assortment Optimization -- Chapter 8: Nonparametric Estimation of Choice Models -- Chapter 9: The MNL-Bandit Problem -- Chapter 10: Dynamic Assortment Optimization: Beyond MNL Model -- Part 4: Inventory Optimization -- Chapter 11: Inventory Control with Censored Demand -- Chapter 12: Joint Pricing and Inventory Control with Demand Learning -- Chapter 13: Optimization in the Small-Data, Large-Scale Regime -- Part 5: Healthcare Operations -- Chapter 14: Bandit Procedures for Designing Patient-Centric Clinical Trials -- Chapter 15: Dynamic Treatment Regimes.Summary: This book examines recent developments in Operations Management, and focuses on four major application areas: dynamic pricing, assortment optimization, supply chain and inventory management, and healthcare operations. Data-driven optimization in which real-time input of data is being used to simultaneously learn the (true) underlying model of a system and optimize its performance, is becoming increasingly important in the last few years, especially with the rise of Big Data.PPN: PPN: 1817570382Package identifier: Produktsigel: ZDB-2-SEB | ZDB-2-BUM | ZDB-2-SXBM

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