The Elements of Joint Learning and Optimization in Operations Management

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 Focuses on pricing, assortment optimization, supply chain and inventory management, and healthcare operations Offers contributions from experts in academia and industry

Auteur

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 provably near-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.

Contenu

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.