Λεπτομέρειες βιβλιογραφικής εγγραφής
| Τίτλος: |
A Primal-Dual Approach Toward Resource-Constrained Revenue Management with Demand Learning and Large Action Space. |
| Συγγραφείς: |
Miao, Sentao1 (AUTHOR) sentao.miao@colorado.edu, Wang, Yining2 (AUTHOR) yining.wang@utdallas.edu, Zhang, Jiawei3 (AUTHOR) jz31@stern.nyu.edu |
| Πηγή: |
Operations Research. Mar/Apr2026, Vol. 74 Issue 2, p825-839. 15p. |
| Θεματικοί όροι: |
*Revenue management, *Time-based pricing, Optimization algorithms, Machine learning, Demand forecasting |
| Περίληψη: |
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. This paper proposes an approach that can be applied to solve several important revenue management (RM) problems with demand learning and potentially large action space constrained by initial unreplenishable resources. This approach combines the technique of the primal-dual method in optimization and upper confidence bound algorithm in learning. Three important RM problems are studied in this paper: network revenue management, dynamic assortment selection with a multinomial-logit choice model, and joint pricing and assortment optimization problems. This paper demonstrates how application-specific subroutines can be developed for each revenue management problem, achieving state-of-the-art theoretical performance guarantees with computationally efficient algorithms. Numerical experiments are conducted to demonstrate that our algorithms have great empirical performance and they are computationally efficient. Funding: S. Miao gratefully acknowledges financial support provided by the Ruegg Family Faculty Scholar Award and the Leeds School of Business. Supplemental Material: All supplemental materials, including the code, data, and files required to reproduce the results, are available at https://doi.org/10.1287/opre.2021.0483. [ABSTRACT FROM AUTHOR] |
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| Βάση Δεδομένων: |
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