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

arXiv: https://arxiv.org/abs/2207.11159 · HTML: https://mlsanity.com/paper/2207.11159/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 88 references that are in the map.
- First posted on arXiv 22 Jul 2022.

## Builds on (most cited first, 25 of 25)

- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Introduction to Online Convex Optimization](https://mlsanity.com/paper/1909.05207/) (2019)
- [A Reductions Approach to Fair Classification](https://mlsanity.com/paper/1803.02453/) (2018)
- [Fairness in Learning: Classic and Contextual Bandits](https://mlsanity.com/paper/1605.07139/) (2016)
- [Bandits with Knapsacks](https://mlsanity.com/paper/1305.2545/) (2013)
- [50 Years of Test (Un)fairness: Lessons for Machine Learning](https://mlsanity.com/paper/1811.10104/) (2018)
- [Linear Contextual Bandits with Knapsacks](https://mlsanity.com/paper/1507.06738/) (2015)
- [Assessing Algorithmic Fairness with Unobserved Protected Class Using Data Combination](https://mlsanity.com/paper/1906.00285/) (2019)
- [The Best of Many Worlds: Dual Mirror Descent for Online Allocation Problems](https://mlsanity.com/paper/2011.10124/) (2020)
- [Calibrated Fairness in Bandits](https://mlsanity.com/paper/1707.01875/) (2017)
- [The Many Faces of Exponential Weights in Online Learning](https://mlsanity.com/paper/1802.07543/) (2018)
- [Individual Fairness in Hindsight](https://mlsanity.com/paper/1812.04069/) (2018)
- [Fair Algorithms for Learning in Allocation Problems](https://mlsanity.com/paper/1808.10549/) (2018)
- [Regularized Online Allocation Problems: Fairness and Beyond](https://mlsanity.com/paper/2007.00514/) (2020)
- [Fair Exploration via Axiomatic Bargaining](https://mlsanity.com/paper/2106.02553/) (2021)
- [Group-Aware Threshold Adaptation for Fair Classification](https://mlsanity.com/paper/2111.04271/) (2021)
- [Fairness, Welfare, and Equity in Personalized Pricing](https://mlsanity.com/paper/2012.11066/) (2020)
- [Fairness and Utilization in Allocating Resources with Uncertain Demand](https://mlsanity.com/paper/1906.09050/) (2019)
- [Fair Allocation through Selective Information Acquisition](https://mlsanity.com/paper/1911.02715/) (2019)
- [Group-level Fairness Maximization in Online Bipartite Matching](https://mlsanity.com/paper/2011.13908/) (2020)
- [Online Pricing with Offline Data: Phase Transition and Inverse Square Law](https://mlsanity.com/paper/1910.08693/) (2019)
- [Fairness-aware Online Price Discrimination with Nonparametric Demand Models](https://mlsanity.com/paper/2111.08221/) (2021)
- [Online Learning Demands in Max-min Fairness](https://mlsanity.com/paper/2012.08648/) (2020)
- [Fairer LP-based Online Allocation via Analytic Center](https://mlsanity.com/paper/2110.14621/) (2021)

## Built on by (most cited first, 3 of 3)

- [Utility Fairness in Contextual Dynamic Pricing with Demand Learning](https://mlsanity.com/paper/2311.16528/) (2023)
- [Fairness-Regularized Online Optimization with Switching Costs](https://mlsanity.com/paper/2512.11131/) (2025)
- [Resource-Adaptive Primal-Dual Learning for One-Warehouse Multi-Store Systems with Censored Demand](https://mlsanity.com/paper/2608.14096/) (2026)

## Abstract (arXiv)

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(N5/2√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