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

Production and Operations Management (Forthcoming)

55 Pages Posted: 9 Sep 2023

See all articles by Xi Chen

Xi Chen

New York University (NYU) - Leonard N. Stern School of Business

Jiameng Lyu

Tsinghua University - Yau Mathematical Sciences Center

Yining Wang

University of Texas at Dallas

Yuan Zhou

Tsinghua University - Yau Mathematical Sciences Center; Tsinghua University - Department of Mathematical Sciences

Date Written: July 22, 2022

Abstract

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 main 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, i.e., 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 (UCB) 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 $\widetilde{O}(N^{5/2}\sqrt{T})$, 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.

Keywords: network revenue management, demand learning, resource-consumption balancing, fairness, regret analysis, linear bandit

Suggested Citation

Chen, Xi and Lyu, Jiameng and Wang, Yining and Zhou, Yuan, Network Revenue Management with Demand Learning and Fair Resource-Consumption Balancing (July 22, 2022). Production and Operations Management (Forthcoming), Available at SSRN: https://ssrn.com/abstract=4565407

Xi Chen

New York University (NYU) - Leonard N. Stern School of Business ( email )

44 West 4th Street
Suite 9-160
New York, NY NY 10012
United States

Jiameng Lyu (Contact Author)

Tsinghua University - Yau Mathematical Sciences Center ( email )

Beijing
China

Yining Wang

University of Texas at Dallas ( email )

2601 North Floyd Road
Richardson, TX 75083
United States

Yuan Zhou

Tsinghua University - Yau Mathematical Sciences Center ( email )

Beijing
China

Tsinghua University - Department of Mathematical Sciences ( email )

Beijing, 100084
China

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