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Record Nr. |
UNINA9910595059703321 |
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Titolo |
The Elements of Joint Learning and Optimization in Operations Management / / edited by Xi Chen, Stefanus Jasin, Cong Shi |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022 |
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ISBN |
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Edizione |
[1st ed. 2022.] |
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Descrizione fisica |
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1 online resource (444 pages) |
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Collana |
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Springer Series in Supply Chain Management, , 2365-6409 ; ; 18 |
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Disciplina |
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Soggetti |
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Production management |
System theory |
Big data |
Business information services |
Operations Management |
Complex Systems |
Big Data |
IT in Business |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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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: |
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Bandit Procedures for Designing Patient-Centric Clinical Trials -- Chapter 15: Dynamic Treatment Regimes. |
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Sommario/riassunto |
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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. |
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