Seminar on October 7 (online) by Linwei Xin, University of Chicago

Speaker: Linwei Xin, University of Chicago

 

Date & Time: October 7, 2022, Friday 17:00

 

Zoom Link:

https://zoom.us/j/6547746234?pwd=ZENZNWtCbUlQRjVMMVFneWtxZGlzZz09

 

Title: The Benefits of Delay to Online Decision-Making

 

Abstract: Real-time decisions are usually irrevocable in many contexts of online decision-making. One common practice is delaying real-time decisions so that the decision-maker can gather more information to make better decisions (for example, in online retailing, there is typically a time delay between when an online order is received and when it gets picked and assembled for shipping). However, decisions cannot be delayed forever. In this paper, we study this fundamental trade-off and aim to theoretically characterize the benefits of delaying real-time decisions.

We provide such a theoretical foundation for a broad family of online decision-making problems by proving that the gap between the proposed online algorithm with delay and the offline optimal hindsight policy decays exponentially fast in the length of delay. We also conduct extensive numerical experiments on the benefits of delay, using both synthetic and real data that is publicly available. Both our theoretical and empirical results suggest that a little delay is all we need.

 

Bio: Linwei Xin is an associate professor of Operations Management at Booth School of Business, University of Chicago. His primary research is on inventory and supply chain management: designing models and algorithms for organizations to effectively “match supply to demand” in various contexts with uncertainty. His research using asymptotic analysis to study stochastic inventory theory has been recognized with several INFORMS paper competition awards, including the Applied Probability Society Best Publication Award (2019), First Place in the George E. Nicholson Student Paper Competition (2015), Second Place in the Junior Faculty Interest Group Paper Competition (2015), and a finalist in the Manufacturing and Service Operations Management Student Paper Competition (2014). His work on implementing state-of-the-art multi-agent deep reinforcement learning techniques in Alibaba’s inventory replenishment system was selected as a finalist for the INFORMS 2022 Daniel H. Wagner Prize, with more than 65% algorithm-adoption rate within Alibaba’s own supermarket brand Tmall Mart. His research with JD.com on dispatching algorithms for robots in intelligent warehouses was recognized as a finalist for the INFORMS 2021 Franz Edelman Award, with an estimate of billions of dollars in savings.

His research motivated by Walmart online grocery’s recommendation-at-checkout-system received the 2017 CSAMSE (Chinese Scholars Association for Management Science and Engineering) Best Paper Award. His research with Argonne National Lab on dynamic line rating received the 2020 IEEE Transactions on Power Systems Best Paper Award.

His other honors include winning a National Science Foundation grant as a principal investigator. His research has been published in journals such as Operations Research, Management Science, Mathematics of Operations Research, and INFORMS Journal on Applied Analytics.

 

Before joining Booth in 2017, Xin was an assistant professor in the College of Engineering at the University of Illinois at Urbana-Champaign. Xin received his PhD in Operations Research from the Georgia Institute of Technology’s H. Milton Stewart School of Industrial and Systems Engineering in 2015.