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The elements of joint learning and optimization in operations management / Xi Chen, Stefanus Jasin, Cong Shi, editors.

Chen, Xi, editor.; Jasin, Stefanus, editor.; Shi, Cong, editor.
2022
TS155
Available Online
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Title
The elements of joint learning and optimization in operations management / Xi Chen, Stefanus Jasin, Cong Shi, editors.
ISBN
9783031019265 (electronic bk.)
3031019261 (electronic bk.)
9783031019258
3031019253
DOI
https://doi.org/10.1007/978-3-031-01926-5
Published
Cham : Springer, [2022]
Copyright
©2022
Language
English
Description
1 online resource (xiii, 444 pages) : illustrations (some color).
Item Number
10.1007/978-3-031-01926-5 doi
Call Number
TS155
Dewey Decimal Classification
658.5
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.
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Access limited to authorized users.
Source of Description
Online resource; title from PDF title page (SpringerLink, viewed October 5, 2022).
Added Author
Chen, Xi, editor.
Jasin, Stefanus, editor.
Shi, Cong, editor.
Series
Springer series in supply chain management ; v. 18.
Available in Other Form
Print version: 9783031019258
ELEMENTS OF JOINT LEARNING AND OPTIMIZATION IN OPERATIONS MANAGEMENT.
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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: Bandit Procedures for Designing Patient-Centric Clinical Trials
Chapter 15: Dynamic Treatment Regimes.

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