Technical Note—An Improved Analysis of LP-Based Control for Revenue Management.

Bounded Regret for LP-Based Revenue-Management Problems In "An Improved Analysis of LP-Based Control for Revenue Management," Chen, Li, and Ye study a class of quantity-based network revenue-management problems. The authors consider a stochastic setting where all the orders are i.i.d. sampled and the customers are of finite type. They focus on the classic LP-based adaptive algorithm and consider regret as the performance measure. They found that when the underlying LP is nondegenerate, the algorithm achieves a problem-dependent regret upper bound that is independent of the horizon/number of time periods T; when the underlying LP is degenerate, the algorithm achieves a tight regret upper bound that scales on the order of T log(T) and matches the lower bound up to a logarithmic order. In this paper, we study a class of revenue-management problems, where the decision maker aims to maximize the total revenue subject to budget constraints on multiple types of resources over a finite horizon. At each time, a new order/customer/bid is revealed with a request of some resource(s) and a reward, and the decision maker needs to either accept or reject the order. Upon the acceptance of the order, the resource request must be satisfied, and the associated revenue (reward) can be collected. We consider a stochastic setting where all the orders are independent and identically distributed-sampled—that is, the reward-request pair at each time is drawn from an unknown distribution with finite support. The formulation contains many classic applications, such as the quantity-based network revenue-management problem and the Adwords problem. We focus on the classic linear program (LP)-based adaptive algorithm and consider regret as the performance measure defined by the gap between the optimal objective value of the certainty-equivalent LP and the expected revenue obtained by the online algorithm. Our contribution is twofold: (i) When the underlying LP is nondegenerate, the algorithm achieves a problem-dependent regret upper bound that is independent of the horizon/number of time periods T; and (ii) when the underlying LP is degenerate, the algorithm achieves a tight regret upper bound that scales on the order of T log (T) and matches the lower bound up to a logarithmic order. To our knowledge, both results are new and improve the best existing bounds for the LP-based adaptive algorithm in the corresponding setting. We conclude with numerical experiments to further demonstrate our findings. Supplemental Material: The online appendix is available at https://doi.org/10.1287/opre.2022.2358. [ABSTRACT FROM AUTHOR]