Single-Leg Revenue Management with Advice
Single-Leg Revenue Management with Advice
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DOI：
10.1145/3580507.3597704
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发表时间：
2022-02
期刊：
影响因子：
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通讯作者：
S. Balseiro;Christian Kroer;Rachitesh Kumar
中科院分区：
文献类型：
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作者：
S. Balseiro;Christian Kroer;Rachitesh Kumar
单程收益管理是收益管理的一个基本问题，在航空公司和酒店业中影响尤为显著：给定n个单位的资源，例如航班座位，以及按票价划分的连续到达的客户流，分配资源的最佳在线策略是什么。以前的工作集中于设计算法，当预测可用时，算法对预测中的不准确不稳健，或者具有最坏情况性能保证的在线算法，这在实践中可能过于保守。在这项工作中，我们通过带建议的算法框架的镜头来看待单腿收入管理问题，该框架试图通过将关于未来的建议最佳地纳入在线算法来利用机器学习方法日益提高的预测精度。特别是，我们开发了在线算法，该算法优化地权衡了每个建议的一致性(建议准确时的性能)和竞争力(建议不准确时的性能)。我们的结果推广到其他单位成本的在线分配问题，如展示广告和多秘书问题，以及更一般的可变成本问题，如在线背包问题。
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 develop online algorithms which optimally trade-off consistency (performance when advice is accurate) and competitiveness (performance when advice is inaccurate) for every advice. 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.