A Primal-Dual Approach Toward Resource-Constrained Revenue Management with Demand Learning and Large Action Space

New Algorithms Advance Revenue Management with Demand Learning A new study by Sentao Miao (University of Colorado Boulder), Yining Wang (University of Texas at Dallas), and Jiawei Zhang (New York University) introduces efficient algorithms for revenue management when firms face limited resources and uncertain demand. Revenue management, used in industries such as airlines, hotels, and retail, requires dynamic decisions on pricing and product assortments, whereas resources such as seats or inventory cannot be replenished. Traditional approaches often struggle with complexity or weak theoretical guarantees. The authors propose a primal-dual learning framework that combines optimization with machine learning’s upper confidence bound method. Their approach achieves near-optimal regret bounds, remaining computationally efficient even in large or infinite decision spaces. Applications include dynamic assortment selection, network revenue management with generalized linear demand, and joint pricing–assortment optimization. Numerical experiments show the methods consistently outperform benchmarks, offering practical, scalable solutions for data-driven industries.