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題目: Data-Driven Scalable E-commerce Transportation Network Design with Unknown Flow Response

主講人：Shuyu Chen，Ph.D

時(shí)間：11月12日（周五）8:30-10:30

地點(diǎn)：bwin必贏唯一官網(wǎng)302室

歡迎廣大師生參加！

Abstract:

Motivated by our experience with a large online marketplace, we study an e-commerce middle-mile transportation network design problem. A salient feature in this problem is decentralized decision-making.  While the middle-mile manager decides the network configuration on a weekly or bi-weekly basis, the real-time flows of millions of packages on any given network configuration (which we call the flow response) are controlled by a fulfillment policy employed by a different decision entity. Thus, we face a fixed-cost network design problem with unknown flow response. To meet this challenge, we first develop a predictive model for the unknown response leveraging machine learning techniques and observed shipment data. We then embed the predictive model to the original network design problem and characterize this transformed problem as a c-supermodular minimization problem. We develop a linear time algorithm with an approximation guarantee that depends on c. In a numerical study, we demonstrate that this algorithm is effective and scalable.

主講人介紹:

Shuyu Chen (陳舒予) is a Ph.D. Candidate in the Operations Management department of the Fuqua School of Business at Duke University. His research focuses on developing and analyzing approximation methods for large-scale stochastic optimization problems, integrating historical data and machine learning methods, with an emphasis on applications in network design and inventory management.