TIME
2025年1月15日14:00-15:30
VENUE
信息管理与工程学院308会议室
SPEAKER
Dr. Mingyang Fu（傅明阳） is a research fellow at the National University of Singapore (NUS), where he completed his PhD in Industrial Systems Engineering. His research interests lie in stochastic and robust optimization, with a particular emphasis on their applications in operations management, such as inventory and revenue management. His research aims to develop robust and innovative solutions to optimize decision-making under uncertainty and with incomplete information.
TITLE
Pricing with Quantiles: Optimal Mechanisms and Pareto Frontiers of Robust Criteria
ABSTRACT
This work addresses a data-driven pricing problem in which a seller aims to sell a single indivisible item to a buyer, using observed price-conversion data. We propose the (r, R, R')-mechanism, a novel class of randomized pricing mechanisms designed to flexibly balance three common robustness criteria: worst-case revenue, worst-case competitive ratio, and worst-case regret. By reducing the original infinite-dimensional problem to a one-dimensional problem, we demonstrate that the family of (r, R, R')-mechanisms can achieve any feasible combination of these performance criteria. This result helps characterize the Pareto frontier among these criteria and provides valuable insights for designing randomized pricing mechanisms under demand uncertainty. In addition, this work also presents an incremental analysis revealing how varying the extent of randomization improves robust performance.
编审：王震 江波
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