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

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

URL: http://arxiv.org/abs/2207.11159v3
Date: Fri, 8 Sep 2023 03:23:37 GMT
Title: Network Revenue Management with Demand Learning and Fair Resource-Consumption Balancing
Authors: Xi Chen, Jiameng Lyu, Yining Wang, Yuan Zhou
Abstract summary: We study the price-based network revenue management (NRM) problem with both demand learning and fair resource-consumption balancing. We propose a primal-dual-type online policy with the Upper-Confidence-Bound (UCB) demand learning method to maximize the regularized revenue. Our algorithm achieves a worst-case regret of $widetilde O(N5/2sqrtT)$, where $N$ denotes the number of products and $T$ denotes the number of time periods.
Score: 16.37657820732206
License: http://creativecommons.org/licenses/by/4.0/
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

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