题　目：Some recent advances in revenue management: pricing with shape restrictions and online resource allocation with nonstationary arrivals and unknown demands

嘉　宾：覃含章，助理教授，新加坡国立大学

主持人：孙海龙 助理教授 上海交通大学安泰经济与管理学院

时　间：2023年12月6日（周三）10:00-11:30am

地　点：安泰经济与管理学院B207

内容简介：

The first half of the talk will be dedicated to a fundamental problem in revenue management: feature-based pricing, where a firm needs to make a pricing decision to maximize the expected revenue for a single product based on feature information. Historical data with price, covariates, and uncensored sales, is available for demand estimation. Our model assumes a linear relationship between price and demand while simultaneously capturing the impact of covariates through a nonparametric shape-restricted function. We develop a Three-Step Semi-Parametric Estimation algorithm to estimate the demand and foster provably near-optimal data-driven pricing decisions. From a non-asymptotic perspective, we derive finite sample regret bounds, showcasing the efficacy of our algorithm in achieving near-optimal revenue, even under potential misspecification of the demand model. The numerical results demonstrate that the decision performance of our algorithm is comparable to the Double Machine Learning method, while significantly outperforming a naive two-step iterative learning method as well. In the second half of the talk, a novel algorithm will be presented for online resource allocation under non-stationary customer arrivals and unknown demands. We assume multiple types of customers arrive in a nonstationary stochastic fashion, with unknown arrival rates in each period, it is also assumed that customers' click-through rates are unknown and can only be learned online. By leveraging results from the stochastic contextual bandit with knapsack and online matching with adversarial arrivals, we develop a online scheme to allocating the resource to nonstationary customers. We prove that under mild conditions, our scheme achieves a “best-of-both-world'' result: the scheme has a sublinear regret when the customer arrivals are near-stationary, and enjoys an optimal competitive ratio under general (non-stationary) customer arrival distributions. Finally, we conduct extensive numerical experiments to show our approach generates near-optimal revenues for all different customer scenarios. The talk is based on joint work with Mabel Chou, Jingren Liu and Xiaoyue Zhang at NUS IORA.

演讲人简介：

Hanzhang Qin is an Assistant Professor at the Department of Industrial Systems Engineering and Management at NUS. He is also an affiliated faculty member at the NUS Institute for Operations Research and Analytics. His research was recognized by several awards, including INFORMS TSL Intelligent Transportation Systems Best Paper Award and MIT MathWorks Prize for Outstanding CSE Doctoral Research. He earned his PhD in Computational Science and Engineering under supervision of Professor David Simchi-Levi, and his research interests span stochastic control, applied probability and statistical learning, with applications in supply chain analytics and transportation systems. He holds two master's, one in EECS and one in Transportation both from MIT. Prior to attending MIT, Hanzhang received two bachelor degrees in Industrial Engineering and Mathematics from Tsinghua University.

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