Title: A 1.79-approximation algorithm for a continuous review lost-sales inventory model

Abstract:

Single-sourcing lost-sales inventory systems with lead times are notoriously difficult to optimize. Recent numerical experiments have suggested that a so-called capped base-stock policy demonstrates superior performance compared with existing heuristics. However, the superior performance lacks of a theoretical foundation (in the stochastic setting) and why such policies generally perform so well remains a major open question. In this paper, we provide a theoretical foundation for this phenomenon. In a continuous review lost-sales inventory model with lead times and Poisson demand, we prove that this policy has a worst-case performance guarantee of 1.79 by conducting an asymptotic analysis under large penalty cost and lead time following Reiman (2004). This result provides a deeper understanding of the superior numerical performance of capped base-stock policies, and presents a new approach to proving worst-case performance guarantees of simple policies in notoriously hard inventory problems.

Bio:

Linwei Xin is an assistant professor of Operations Management at the University of Chicago Booth School of Business. He graduated from ISyE in 2015, advised by David A. Goldberg and Alexander Shapiro. His research interests include supply chain, inventory and revenue management, optimization under uncertainty, and data-driven decision-making. His work has been recognized with several INFORMS paper competition awards, including the 2019 Applied Probability Society Best Publication Award, First Place in the 2015 George E. Nicholson Student Paper Competition, Second Place in the 2015 Junior Faculty Interest Group Paper Competition, and a finalist in the 2014 Manufacturing and Service Operations Management Student Paper Competition. His research has been published in journals such as Operations Research and Management Science. He won a NSF grant as PI. He also has worked with companies/organizations through research collaboration including Alibaba Group and Walmart Global eCommerce.