Single-leg revenue management is a foundational problem of revenue management that has been particularly impactful in the airline and hotel industry: Given $n$ units of a resource, e.g. flight seats, and a stream of sequentially-arriving customers segmented by fares, what is the optimal online policy for allocating the resource. Previous work focused on designing algorithms when forecasts are available, which are not robust to inaccuracies in the forecast, or online algorithms with worst-case performance guarantees, which can be too conservative in practice. In this work, we look at the single-leg revenue management problem through the lens of the algorithms-with-advice framework, which attempts to harness the increasing prediction accuracy of machine learning methods by optimally incorporating advice about the future into online algorithms. In particular, we characterize the Pareto frontier that captures the tradeoff between consistency (performance when advice is accurate) and competitiveness (performance when advice is inaccurate) for every advice. Moreover, we provide an online algorithm that always achieves performance on this Pareto frontier. We also study the class of protection level policies, which is the most widely-deployed technique for single-leg revenue management: we provide an algorithm to incorporate advice into protection levels that optimally trades off consistency and competitiveness. Moreover, we empirically evaluate the performance of these algorithms on synthetic data. We find that our algorithm for protection level policies performs remarkably well on most instances, even if it is not guaranteed to be on the Pareto frontier in theory. Our results extend to other unit-cost online allocations problems such as the display advertising and the multiple secretary problem together with more general variable-cost problems such as the online knapsack problem.

翻译：单腿收益管理是收益管理领域的基础问题，尤其在航空和酒店行业影响深远：给定n个资源单位（如航班座位）和按票价分组的顺序到达客户流，如何制定最优在线资源分配策略。以往研究主要聚焦于两类方法：基于预测可用性设计的算法（对预测误差缺乏鲁棒性），或具有最坏情形性能保障的在线算法（实践中可能过于保守）。本研究从"算法与建议框架"视角审视单腿收益管理问题，该框架通过将关于未来的建议最优地融入在线算法，旨在利用机器学习方法日益提升的预测精度。具体而言，我们针对每条建议刻画了捕获一致性（建议准确时的性能）与竞争性（建议不准时的性能）权衡的帕累托前沿，并提供了始终能在此前沿上实现性能的在线算法。同时研究了保护水平策略（单腿收益管理最广泛使用的技术）：我们提出一种将建议融入保护水平并实现一致性-竞争性最优权衡的算法。进一步在合成数据上对这些算法进行实证评估，发现保护水平策略的算法在多数实例中表现优异，尽管理论上无法保证完全处于帕累托前沿上。本研究结果可扩展至其他单位成本在线分配问题（如展示广告、多秘书问题）以及更一般的可变成本问题（如在线背包问题）。