Overview
- Presents a novel use of real-time learning and optimization
- Focuses on pricing, assortment optimization, supply chain and inventory management, and healthcare operations
- Offers contributions from experts in academia and industry
Part of the book series: Springer Series in Supply Chain Management (SSSCM, volume 18)
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About this book
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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Keywords
Table of contents (15 chapters)
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Front Matter
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Generic Tools
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Front Matter
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Assortment Optimization
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Front Matter
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Inventory Optimization
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Front Matter
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Healthcare Operations
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Front Matter
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Editors and Affiliations
About the editors
Xi Chen is an Assistant Professor of Information, Operations and Management Sciences in New York University Stern School of Business (US). Professor Chen studies machine learning and optimization, high-dimensional statistics and operations research. He is developing parametric and non-parametric statistical methods as well as efficient optimization algorithms to address challenges in high-dimensional data analysis. He also works on statistical learning and online decision-making for crowdsourcing. He also investigates operations research/management problems, such as the optimal network design in process flexibility, approximate dynamic programming and revenue management.
Stefanus Jasin is an Assistant Professor of Technology and Operations at the Ross School of Business, University of Michigan, Ann Arbor (US). He is broadly interested in many topics that lie at the intersection of OR, OM, IS, and Marketing, with an emphasis on developing provablynear-optimal and easily implementable heuristic controls. Some of his works include: real-time pricing, e-commerce order fulfillment, assortment optimization, delivery consolidation, inventory optimization, and joint learning and optimization. Most recently, he is also working on optimization in the on-demand market.
Cong Shi is an Associate Professor at the University of Michigan (US). His research is focused on the design of efficient algorithms with theoretical performance guarantees for stochastic optimization models in operations management. Main areas of applications include inventory control, supply chain management, revenue management, and service operations.
Bibliographic Information
Book Title: The Elements of Joint Learning and Optimization in Operations Management
Editors: Xi Chen, Stefanus Jasin, Cong Shi
Series Title: Springer Series in Supply Chain Management
DOI: https://doi.org/10.1007/978-3-031-01926-5
Publisher: Springer Cham
eBook Packages: Business and Management, Business and Management (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022
Hardcover ISBN: 978-3-031-01925-8Published: 21 September 2022
Softcover ISBN: 978-3-031-01928-9Published: 22 September 2023
eBook ISBN: 978-3-031-01926-5Published: 20 September 2022
Series ISSN: 2365-6395
Series E-ISSN: 2365-6409
Edition Number: 1
Number of Pages: XIII, 444
Number of Illustrations: 11 b/w illustrations, 10 illustrations in colour
Topics: Supply Chain Management, Data-driven Science, Modeling and Theory Building, Big Data, Big Data/Analytics