论文标题
不断遗憾地重新解决基于价格的收入管理的启发式方法
Constant Regret Re-solving Heuristics for Price-based Revenue Management
论文作者
论文摘要
基于价格的收入管理是许多实际应用的运营管理中的重要问题。该问题考虑了连续$ t $销售产品（或多种产品）的零售商，并且在初始库存水平上受到限制。虽然可以通过动态编程获得最佳定价策略，但由于高计算成本，这种方法有时是不受欢迎的。近似策略（例如重新解决的启发式方法）通常被用作计算上的替代方案。在本文中，我们显示以下两个结果。首先，我们证明，与最佳政策的价值相比，自然重新解决的启发式使$ O（1）$遗憾。这改善了\ cite {jasin2014 reoptimization}的$ o（\ ln t）$遗憾的上限。其次，我们证明，最佳策略和流体模型的值之间存在$ω（\ ln T）$差距。这可以通过表明流体在分析基于价格的收入管理算法时表明流体并不是足够的信息删除的基准来补充我们的上限结果。
Price-based revenue management is an important problem in operations management with many practical applications. The problem considers a retailer who sells a product (or multiple products) over $T$ consecutive time periods and is subject to constraints on the initial inventory levels. While the optimal pricing policy could be obtained via dynamic programming, such an approach is sometimes undesirable because of high computational costs. Approximate policies, such as the re-solving heuristics, are often applied as computationally tractable alternatives. In this paper, we show the following two results. First, we prove that a natural re-solving heuristic attains $O(1)$ regret compared to the value of the optimal policy. This improves the $O(\ln T)$ regret upper bound established in the prior work of \cite{jasin2014reoptimization}. Second, we prove that there is an $Ω(\ln T)$ gap between the value of the optimal policy and that of the fluid model. This complements our upper bound result by showing that the fluid is not an adequate information-relaxed benchmark when analyzing price-based revenue management algorithms.
