Prof. Yue Hu
Operations, Information and Technology
Stanford Graduate School of Business
Talk:
Online Learning for Dynamic Service Mode Control
Abstract:
Modern service systems are increasingly adopting innovative modalities enabled by emerging technologies, such as AI-assisted services, to achieve a better balance between quality and efficiency. Motivated by these advancements, we study the optimal dynamic control of service modes in systems with unknown parameters, aiming to balance reward accumulation and system congestion. We consider a single-server queueing system with two switchable service modes, each characterized by a distinct service rate and an unknown distribution of rewards earned upon service completion. The objective is to maximize the long-run average of cumulative rewards minus holding costs. To address this problem, we first characterize the optimal state-dependent policy under full knowledge of model parameters. For the case of unknown reward distributions, we propose an online learning algorithm based on Upper Confidence Bound estimates to adaptively learn the optimal policy. Our algorithm achieves a statistically near-optimal regret bound of \tilde{O}(\sqrt{T}) over a time horizon T and demonstrates strong numerical performance. A key methodological contribution is a novel regret decomposition and a regenerative cycle-based framework, offering broader insights into learning-based optimal control in queueing systems. Finally, we demonstrate the practical relevance of our approach through a case study on optimizing AI-assisted patient message replies in a healthcare setting.
Biography:
Professor Yue Hu is an Assistant Professor of Operations, Information, and Technology at Stanford University’s Graduate School of Business. Her primary research interests are in data-driven stochastic modeling of service systems with an emphasis on healthcare operations management. She completed her PhD in Decision, Risk, and Operations from Columbia Business School, and undergraduate studies at Northwestern University.
