Dear all,

You are cordially invited to an onsite academic seminar to be delivered by Dr. Yue Hu on June 19 (Wednesday). Details could be found below.

Seminar Information

Time and Date: 14:00 PM - 15:30 PM, June 19, 2024 (Wednesday)

Venue: Room 204, Teaching A Building

Speaker: Dr. Yue Hu (Stanford University)

Topic: Value of Sparse Structures in Dynamic Reusable Resource Allocation with Waiting

Zoom Access
Link: https://cuhk-edu-cn.zoom.us/j/6517018806?pwd=WkNDSC8zMTlIVTYzK2kyMWo5YjlVQT09

Meeting ID: 651 701 8806
Passcode: 123456

About the Speaker

Yue Hu is an Assistant Professor of Operations, Information & Technology at Stanford Graduate School of Business. Her research lies at the intersection of healthcare operations management and applied probability. With particular focus on scheduling, staffing, and patient-flow management in healthcare delivery systems, she studies how to leverage predictive analytics to guide operational strategies and innovations. In addition to solving practically relevant problems, she conducts research in developing new methodologies for the approximation and control of stochastic systems. Hu’s research has been recognized in a number of competitions, including as the finalist of the 2022 INFORMS Doing Good with Good OR Competition, winner of the 2020 INFORMS APS Best Student Paper Award, finalist of the 2019 INFORMS IBM Best Student Paper Award, and honorable mention in the 2017 INFORMS Undergraduate Operations Research Prize. Hu received her PhD from the Decision, Risk and Operations Division at the Graduate School of Business, Columbia University. Prior to pursuing her PhD, she received a BS from the Department of Industrial Engineering and Management Sciences at Northwestern University.

Abstract

We study the dynamic resource allocation problem in online service platforms featuring reusable resources, waiting space, and heterogeneous demands and resources. The service provider aims to balance the trade-off between maximizing revenue and minimizing waiting times across various demand types. For this problem, we propose a comprehensive framework to minimize the long-run average revenue loss and waiting cost by simultaneously designing the 1) flexibility structure, 2) admission control, and 3) scheduling policy. The flexibility structures are tailored to systems with varying levels of workload intensity, and the number of arcs in the network is linear in the number of resource and demand types. In these sparse networks, we show that simple static priority rules and threshold-based admission controls are asymptotically optimal in the many-server regimes. Furthermore, our proposed algorithm for designing system flexibility, along with the scheduling and admission control policies, is both easy to interpret and straightforward to implement. Numerical experiments demonstrate the effectiveness of our approach, particularly for smaller systems, in non-asymptotic environments. (This is joint work with Shixin Wang (CUHK) and Jing Dong (Columbia).)