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xi c elements joint learning optimization 2022

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Textbook in PDF format 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. Preface Editors and Contributors About the Editors Contributors Generic Tools The Stochastic Multi-Armed Bandit Problem Introduction The N-Armed Bandit Problem Upper Confidence Bound (UCB) Algorithm Thompson Sampling (TS) Contextual Bandits Combinatorial Bandits References Reinforcement Learning Introduction Markov Decision Process and Dynamic Programming Finite-Horizon Markov Decision Process Dynamic Programming Solution Discounted Markov Decision Process Value Iteration Policy Iteration Reinforcement Learning Algorithm Design Reinforcement Learning Problem Formulation Episodic Reinforcement Learning in Finite-Horizon MDP Reinforcement Learning in Discounted MDP Model-Based vs Model-Free Reinforcement Learning Model-Based Reinforcement Learning Q-Learning and SARSA Policy Gradient Exploration in Reinforcement Learning Exploration Schemes Deep Exploration Approximate Solution Methods and Deep Reinforcement Learning Conclusion and Further Reading References Optimal Learning and Optimal Design Introduction Statistical Design of Experiments The Ranking and Selection Problem Model Large Deviations Analysis Example: Normal Sampling Distributions Optimal Allocations Sequential Algorithms Value of Information Methods Thompson Sampling Rate-Balancing Methods Discussion Recent Advances A New Optimal Design for Linear Regression Optimal Budget Allocation in Approximate Dynamic Programming Conclusion References Price Optimization Dynamic Pricing with Demand Learning: Emerging Topics and State of the Art Introduction Model Asymptotically Optimal Pricing Policies Parametric Approaches Model and Estimation Certainty-Equivalence Pricing and Incomplete Learning Asymptotically Optimal Policies Extensions to Generalized Linear Models Extensions to Multiple Products Nonparametric Approaches Extensions and Generalizations Emerging Topics and Generalizations Product Differentiation Online Marketplaces Continuous-Time Approximations References Learning and Pricing with Inventory Constraints Introduction Single Product Case Dynamic Pricing Algorithm Lower Bound Example Multiproduct Setting Preliminaries Parametric Case Nonparametric Case Bayesian Learning Setting Model Setting Thompson Sampling with Fixed Inventory Constraints Thompson Sampling with Inventory Constraint Updating Performance Analysis Remarks and Further Reading References Dynamic Pricing and Demand Learning in Nonstationary Environments Introduction Problem Formulation Exogenously Changing Demand Environments Change-Point Detection Models Finite-State-Space Markov Chains Autoregressive Models General Changing Environments Contextual Pricing Endogenously Changing Demand Environments Reference-Price Effects Competition and Collusion Platforms and Multi-Agent Learning Forward-Looking and Patient Customers References Pricing with High-Dimensional Data Introduction Background: High-Dimensional Statistics Static Pricing with High-Dimensional Data Feature-Dependent Choice Model Estimation Method Performance Guarantees Dynamic Pricing with High-Dimensional Data Feature-Dependent Demand Model Learning-and-Earning Algorithm A Universal Lower Bound on the Regret Performance of ILQX Discussion Directions for Future Research References Assortment Optimization Nonparametric Estimation of Choice Models Introduction General Setup Estimating the Rank-Based Model Estimation via the Conditional Gradient Algorithm Solving the Support Finding Step Solving the Proportions Update Step Initialization and Stopping Criterion Convergence Guarantee for the Estimation Algorithm Estimating the Nonparametric Mixture of Closed Logit (NPMXCL) Model Estimation via the Conditional Gradient Algorithm Solving the Support Finding Step Solving the Proportions Update Step Initialization and Stopping Criterion Convergence Guarantee for the Estimation Algorithm Characterizing the Choice Behavior of Closed Logit Types Other Nonparametric Choice Models Concluding Thoughts References The MNL-Bandit Problem Introduction Choice Modeling and Assortment Optimization Dynamic Learning in Assortment Selection A UCB Approach for the MNL-Bandit Algorithmic Details Min–Max Regret Bounds Improved Regret Bounds for ``Well Separated'' Instances Computational Study Robustness of Algorithm Comparison with Existing Approaches Thompson Sampling for the MNL-Bandit Algorithm A TS Algorithm with Independent Beta Priors A TS Algorithm with Posterior Approximation and Correlated Sampling Regret Analysis Empirical Study Lower Bound for the MNL-Bandit Conclusions and Recent Progress References Dynamic Assortment Optimization: Beyond MNL Model Overview General Utility Distributions Model Formulation and Assumptions Algorithm Design Theoretical Analysis Bibliographic Notes and Discussion of Future Directions Nested Logit Models Model Formulation and Assumptions Assortment Space Reductions Algorithm Design and Regret Analysis Regret Lower Bound Bibliographic Notes and Discussion of Future Directions MNL Model with Contextual Features Model Formulation and Assumptions Algorithm Design: Thompson Sampling Algorithm Design: Upper Confidence Bounds Lower Bounds Bibliographic Notes and Discussion of Future Directions Conclusion References Inventory Optimization Inventory Control with Censored Demand Introduction Regret Lower Bound for Inventory Models with Censored Demand Model Formulation Strictly Convex and Well-Separated Cases Worst-Case Regret Under General Demand Distributions Censored Demand Example: Perishable Inventory System Model Formulation Challenges and Preliminary Results Learning Algorithm Design: Cycle-Update Policy Regret Analysis of CUP Algorithm Strongly Convex Extension Lead Times Example: Lost-Sales System with Lead Times Model Formulation Base-Stock Policy and Convexity Results Challenges from Lead Times Gradient Methods A Ternary Search Method High Dimensionality Example: Multiproduct Inventory Model with Customer Choices Inventory Substitution Numerical Example References Joint Pricing and Inventory Control with Demand Learning Problem Formulation in General Nonparametric Learning for Backlogged Demand Nonparametric Learning for Lost-Sales System Algorithms and Results in chennonparametric Algorithms and Results in chenoptimal Concave G(·) Non-Concave G(·) Parametric Learning with Limited Price Changes Well-Separated Demand General Demand Backlog System with Fixed Ordering Cost Other Models References Optimization in the Small-Data, Large-Scale Regime Why Small Data? Structure Contrasting the Large-Sample and Small-Data, Large-Scale Regimes Model Failure of Sample Average Approximation (SAA) Best-in-Class Performance Shortcomings of Cross-Validation Debiasing In-Sample Performance Stein Correction From Unbiasedness to Policy Selection Stein Correction in the Large-Sample Regime Open Questions Conclusion References Healthcare Operations Bandit Procedures for Designing Patient-Centric Clinical Trials Introduction The Bayesian Beta-Bernoulli MABP Discussion of the Model Metrics for Two-Armed Problem (Confirmatory Trials) Accurate and Precise Estimation Statistical Errors Patient Benefit Trial Size Multiple Metrics Illustrative Results for Two-Armed Problem Discussion Safety Concerns Prior Distributions Delayed Responses Dropouts and Missing Responses Early Evidence of Efficacy or Futility Non-binary Outcomes Exploratory Trials Large Trials References Dynamic Treatment Regimes for Optimizing Healthcare Introduction Mathematical Framework Potential Outcomes Framework Data Sources for Constructing DTRs Longitudinal Observational Data The CIBMTR Registry: Two Study Examples for Constructing DTRs with Observational Data Sequentially Randomized Studies The SMART Weight Loss Management Study Dynamical Systems Models A Dynamical Systems Model for Behavioral Weight Change Methods for Constructing DTRs Origins and Development of DTRs Reinforcement Learning: A Potential Solution Taxonomy of Existing Methods Finite-Horizon DTRs Indirect Methods Direct RL Methods Indefinite-Horizon DTRs Inference in DTRs Inference for Parameters Indexing the Optimal Regime Inference for the Value Function of a Regime Practical Considerations and Final Remarks Model Choice and Variable Selection Sample Size Considerations and Power Analysis Missing Data Additional Issues and Final Remarks References

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