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.

Presents a novel use of real-time learning and optimization

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.