Network Revenue Management with Nonparametric Demand Learning: √T-Regret and Polynomial Dimension Dependency

• 成果类型: Article; Early Access
• 署名作者: Miao, Sentao; Wang, Yining
• 署名单位: University of Colorado System; University of Colorado Boulder
• 刊物名称: MATHEMATICS OF OPERATIONS RESEARCH
• ISSN/ISSBN: 0364-765X
• DOI: 10.1287/moor.2022.0086
• 发表日期: 2025
• 关键词: Bounds; optimization; algorithm; policies

摘要

This paper studies the classic price-based network revenue management (NRM) problem with demand learning. The retailer dynamically decides prices of n products over a finite selling season (of length T) subject to m resource constraints, with the purpose of maximizing the cumulative revenue. In this paper, we focus on a nonparametric demand model with some mild technical assumptions which are satisfied by most of the commonly used demand functions. We propose a robust ellipsoid method adapted to the NRM setting in a nontrivial manner. This is the first result which achieves the regret of the form root ffiffiffi O(poly(n,m,ln(T)) T ) (where poly(n,m,ln(T)) is a polynomial function of n,m,ln(T)) in the current literature on the nonparametric NRM problem.