Faculty & Research

Linwei Xin

Assistant Professor of Operations Management

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5807 South Woodlawn Avenue
Chicago, IL 60637

Linwei Xin is an assistant professor of Operations Management. His research interests include supply chain and inventory management, optimization under uncertainty, and data-driven decision-making. His work has been recognized with several INFORMS paper competition awards, including First Place in the 2015 George E. Nicholson Student Paper Competition, Second Place in the 2015 JFIG Paper Competition, and a finalist in the 2014 MSOM Student Paper Competition. His research has been accepted/published in journals such as Operations Research, Management Science, and Operations Research Letters. He won a $330,654.00 NSF grant as PI. He also has worked with companies/organizations through research collaboration or consulting including Walmart Global eCommerce, IBM, Boxed Wholesale, and JD.com.

Before joining Booth in 2017, Xin was a faculty member at the University of Illinois at Urbana-Champaign where he was on the List of Teachers Ranked as Excellent by Their Students. While at the University of Illinois, he taught Stochastic Processes and Applications and Advanced Production Planning and Control. He also worked for Walmart Labs as a data scientist intern in 2015 and IBM Research as a research intern in 2013.

Xin earned a PhD in operations research from the Georgia Institute of Technology’s H. Milton Stewart School of Industrial and Systems Engineering in 2015 and a bachelor’s degree in mathematics from Zhejiang University in 2008. He also pursued PhD studies in mathematics at Georgia Tech prior to his operations research studies.


2017 - 2018 Course Schedule

Number Name Quarter
36600 Workshop in Operations/Management Science 2017 (Fall)
36600 Workshop in Operations/Management Science 2018 (Spring)
40000 Operations Management: Business Process Fundamentals 2018 (Spring)

REVISION: Dynamic Recommendation at Checkout under Inventory Constraint
Date Posted: Nov  11, 2016
This work is motivated by a new checkout recommendation system at Walmart's online grocery, which offers a customer an assortment of up to 8 items that can be added to an existing order, at potentially discounted prices. We formalize this as an online assortment planning problem under limited inventory, with customer types defined by the items initially selected in the order. Multiple item prices, combined with customer withdrawal when their initially selected items stock out, pose additional challenges for the development of an online policy. We overcome these challenges by introducing the notion of an inventory protection level in expectation, and presenting an algorithm with bounded competitive ratio when the arrival sequence is chosen adversarially. We further conduct numerical experiments which compare the performance of our algorithm with several existing benchmarks.

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