Network Revenue Management

Network revenue management (NRM) optimizes pricing and resource allocation to maximize revenue in systems with multiple products and limited resources. Current research emphasizes developing efficient algorithms, particularly reinforcement learning and primal-dual optimization methods, to handle the complexity of large-scale NRM problems with uncertain, potentially non-parametric demand. A key focus is improving regret bounds—the difference between achieved and optimal revenue—through techniques like demand balancing and refined learning approaches. These advancements offer significant potential for improving operational efficiency and revenue generation across various industries, including retail, transportation, and cloud computing.