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
||Workshop in Operations/Management Science
||Workshop in Operations/Management Science
||Operations Management: Business Process Fundamentals
New: Asymptotic Optimality of Order-Up-To Control for Stochastic Inventory Systems with Sequential Probabilistic Service Level Constraints
We consider a stochastic inventory model (under backorder and lost-sales) with non-stationary demands, positive lead time, and sequential probabilistic service level constraints. This is a notoriously difficult problem to solve and, to date, not much progress has been made in understanding the structure of its optimal control, especially for the lost-sales inventory system. In this paper, we propose a simple order-up-to control, whose parameters can be calculated using the optimal solution of a deterministic approximation of the backorder inventory system, and show that it is asymptotically optimal for both the backorder and lost-sales systems in the regime of high service level requirement. This result contributes to the growing body of inventory literature that show the near-optimality of simple heuristic controls. Moreover, it also gives credence to the use of deterministic approximation for solving complex inventory problems in practice, at least for applications where the ...
New: On the Performance of Tailored Base-Surge Policies: Theory and Application at Walmart.Com
Problem Definition: Many import items sold at Walmart.com have two suppliers: one is faster and has a random production capacity limit while the other is slower and has infinite capacity. The lead time difference of the two suppliers could be as large as 12 weeks. Walmart.com faces an important and challenging supply chain problem and must make routine inventory replenishment decisions.
Academic/Practical Relevance: Dual-sourcing inventory models have been extensively studied in academia and dual-sourcing strategies have been widely implemented in practice. Recently, the so-called Tailored Base-Surge (TBS) policy has begun to receive more attention and demonstrated good performance in existing dual-sourcing models. TBS is intuitive and easy to manage: a constant-order is placed from the slow (regular) supplier every period and a base-stock policy is adapted from the fast (express) supplier.
Methodology: This paper develops a new dual-sourcing inventory model incorporating large ...
REVISION: Dynamic Recommendation at Checkout under Inventory Constraint
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.