Network Revenue Management With Demand Learning and Fair Resource-Consumption Balancing

• 成果类型: Article
• 署名作者: Chen, Xi; Lyu, Jiameng; Wang, Yining; Zhou, Yuan
• 署名单位: New York University; Tsinghua University; University of Texas System; University of Texas Dallas; Tsinghua University; Tsinghua University; Tsinghua University
• 刊物名称: PRODUCTION AND OPERATIONS MANAGEMENT
• ISSN/ISSBN: 1059-1478
• DOI: 10.1177/10591478231225176
• 发表日期: 2024
• 页码: 494-511
• 关键词: Network Revenue Management; Demand Learning; Resource-Consumption Balancing; fairness; Regret Analysis; Linear Bandit

摘要

In addition to maximizing the total revenue, decision-makers in lots of industries would like to guarantee balanced consumption across different resources. For instance, in the retailing industry, ensuring a balanced consumption of resources from different suppliers enhances fairness and helps maintain a healthy channel relationship; in the cloud computing industry, resource-consumption balance helps increase customer satisfaction and reduce operational costs. Motivated by these practical needs, this paper studies the price-based network revenue management (NRM) problem with both demand learning and fair resource-consumption balancing. We introduce the regularized revenue, that is, the total revenue with a balancing regularization, as our objective to incorporate fair resource-consumption balancing into the revenue maximization goal. We propose a primal-dual-type online policy with the upper-confidence-bound demand learning method to maximize the regularized revenue. We adopt several innovative techniques to make our algorithm a unified and computationally efficient framework for the continuous price set and a wide class of balancing regularizers. Our algorithm achieves a worst-case regret of (O) over tilde (N-5/2 root T), where N denotes the number of products and T denotes the number of time periods. Numerical experiments in a few NRM examples demonstrate the effectiveness of our algorithm in simultaneously achieving revenue maximization and fair resource-consumption balancing.