报告人
Sentao Miao 助理教授
主持人
陈彩华 教授
时间
1月10日（周五) 10:00-12:00
地点
协鑫楼204
Beyond One-Size-Fits-All: Personalized Delivery and Fulfillment Optimization
报告摘要
Motivated by our collaboration with an online platform operating in North America, we explore the joint optimization of the order fulfillment process with personalized delivery options in the context of e-commerce. Customers can choose from personalized fulfillment options to proceed with the purchase or leave with no purchase. The retailer periodically makes fulfillment decisions and relies on multiple logistic providers to perform the fulfillment operations. We model customer behavior with a general discrete choice model and formulate the joint optimization as a stochastic dynamic program. We propose a tractable deterministic approximation and develop a computationally efficient heuristic with a provable performance guarantee. We also extend the proposed heuristic to scenarios when customer behaviors are more complex and affected by fulfillment speed, cost, and order value. Using real datasets collected from our industrial partner, we demonstrate the value of personalizing fulfillment options for the customers and jointly optimizing the options with fulfillment assignments. Our results show that demand management via personalized fulfillment options is prominent when customers favor quicker fulfillment and when the fulfillment capacity is limited. However, an optimized fulfillment operation becomes more critical when customers are more willing to wait.
报告人简介
Sentao Miao is an Assistant Professor of Operations Management in Leeds School of Business at University of Colorado Boulder. Previously, he was an Assistant Professor in Bensadoun School of Retail Management & Desautels Faculty of Management at McGill University. His research interests are mainly in developing efficient learning and optimization algorithms with various applications in Operations Management. For methodologies, Sentao Miao focuses on statistical and machine learning algorithms such as online learning, multi-arm bandit problem, reinforcement learning.
美编 | 李梦爽
责编 | 李梦爽、唐迪明
