主讲人

（单位）

刘方

(杜伦大学)

主持人

（单位）

陈静

（电影天堂 ）

讲座时间

2026年11月5日上午10点

讲座地点

综合楼310

主讲人简介

Fang Liu is currently a professor in Operations Management at the Durham University Business School, Durham University. She received her PhD in Operations Management from Fuqua School of Business, Duke University. Her primary research interests are in humanitarian operations, mechanism design, and sustainable operations. She currently serves as an Associate Editor for Omegaand Decision Sciences. She is currently working on multiple projects, including various allocation problems with applications in humanitarian operations and e-commerce. Her paper has appeared in Operations Research, Manufacturing and Service Operations Management, and Production and Operations Management. She is the winner of 2022 M&SOM data-driven competition and the finalist of IFORS Prize for Development 2023.

讲座内容摘要

The rapid growth of e-commerce has significantly expanded product variety, intensifying the challenge of efficient order fulfillment. To enable fast delivery, e-retailers strategically position front-end warehouses near customers. However, these warehouses operate under strict capacity constraints, making assortment selection a critical decision in minimizing fulfillment costs. Ideally, each customer order can be fulfilled entirely from a single front-end warehouse. Otherwise, reliance on back-end support increases operational costs, carbon emissions, and delivery lead times, ultimately degrading service quality. In this work, we propose a scalable and flexible heuristic for warehouse assortment selection in a multi-warehouse setting. The heuristic accommodates diverse warehouse- and order-specific cost structures while providing rigorous performance guarantees. Specifically, we establish distribution-free approximation guarantees for normalized additional fulfillment cost: the factor is bounded by the maximum order size in a single-front-end-warehouse system and by the maximum order size divided by the positive normalized fulfillment-cost gap between the unique cheapest and second-cheapest warehouses in a multi-warehouse system. Extensive numerical experiments further demonstrate that the proposed heuristic consistently outperforms existing methods across both single- and multi-warehouse settings.