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時(shí)間

TIME

1月28日（星期四）09:00-11:00

地點(diǎn)

VENUE

Zoom會(huì)議室號(hào)碼：96266354188

密碼：177856

主講人

SPEAKER

Shuyu Chenis a Ph.D. Candidate in the Operations Management department of the Fuqua School of Business at Duke University. He received a Bachelor’s degree in Industrial Engineering from National University of Singapore. 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.

主題

TITLE

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

摘要

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 monthly basis, the real-time flows of millions of packages on any given network configuration 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 using observed shipment data. Apart from the most natural network-level predictive model, we find that the more parsimonious arc-level flow response predictive model is more effective. We then embed the predictive model to the original network design model to obtain a transformed problem. We characterize this new problem as a c-supermodular minimization problem and develop two linear time approximation algorithms with performance guarantees. We demonstrate that these two algorithms are scalable and effective in numerical studies. Our approach is also applicable to omni-channel retailers which may have thousands of brick-and-mortar stores as the destinations for customers to pick up online orders.