Abstract:
I will talk about two recent research papers in e-commerce order fulfillment.
(1) “Multi-Item Online Order Fulfillment in a Two-Layer Network”
The boom of e-commerce in the globe in recent years has expedited the expansion of fulfillment infrastructures by e-retailers. While e-retailers are building more and more mini-warehouses close to end customers to offer faster delivery service than ever, the associated fulfillment costs have skyrocketed. In this paper, we study a real-time fulfillment problem in a two-layer RDC-FDC distribution network that has been implemented in practice by major e-retailers. In such a network, the upper layer contains larger regional distribution centers (RDCs) and the lower layer contains smaller front distribution centers (FDCs). We allow order split: an order can be split and fulfilled from multiple warehouses at an additional cost. The objective is to minimize the routine fulfillment costs. We study real-time algorithms with performance guarantees in both settings with and without demand forecasts.
This is joint work with Yanyang Zhao (Chicago) and Xinshang Wang (Alibaba). An old version of the paper can be found here: https://papers.ssrn.com/sol3/Papers.cfm?abstract_id=3675117.
(2) “Designing Maximum Matchings in Sparse Random Graphs”
This is based on a research study partially supported by an Alibaba Cainiao Research Grant. We consider the problem of designing a sparse graph that supports a large matching with random-node disruptions. It is motivated by a middle-mile transportation problem for an e-commerce platform. We study three families of graph designs and characterize their theoretical performance. Our theoretical results suggest that their performance is close to that of a complete graph. We also complement our theoretical study and evaluate their empirical performance in a middle-mile transportation problem by using real-data from Cainiao. This is join work with Yifan Feng (NUS), René Caldentey (Chicago), Yuan Zhong (Chicago), and Alibaba Cainiao.
Bio: Linwei Xin is an assistant professor of Operations Management at the University of Chicago Booth School of Business. 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 on stochastic inventory theory by using asymptotic analysis 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 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 other honors include winning a National Science Foundation grant as a principal investigator. His research has been published in journals such as Operations Research and Management Science.