IEDA Webinar - The Benefits of Delay to Online Decision-Making

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 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 demonstrate an important managerial insight: a little delay is all we need. Finally, we extend our analysis and results to the setting with unknown demand distribution and the setting in which decisions are made in batches. This is joint work with Yaqi Xie (Chicago Booth School of Business) and Will Ma (Columbia Business School).


The paper is available at:

講者/ 表演者:
Prof. Linwei Xin
Booth School of Business, University of Chicago

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 JFIG Paper Competition (2015), and a finalist in the MSOM 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 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 has been published in journals such as Operations Research, Management Science, Mathematics of Operations Research, and INFORMS Journal on Applied Analytics.

Department of Industrial Engineering & Decision Analytics