This paper discusses the revenue management (RM) problem to maximize revenue
by pricing items or services. One challenge in this problem is that the demand
distribution is unknown and varies over time in real applications such as
airline and retail industries. In particular, the time-varying demand has not
been well studied under scenarios of unknown demand due to the difficulty of
jointly managing the remaining inventory and estimating the demand. To tackle
this challenge, we first introduce an episodic generalization of the RM problem
motivated by typical application scenarios. We then propose a computationally
efficient algorithm based on posterior sampling, which effectively optimizes
prices by solving linear programming. We derive a Bayesian regret upper bound
of this algorithm for general models where demand parameters can be correlated
between time periods, while also deriving a regret lower bound for generic
algorithms. Our empirical study shows that the proposed algorithm performs
better than other benchmark algorithms and comparably to the optimal policy in
hindsight. We also propose a heuristic modification of the proposed algorithm,
which further efficiently learns the pricing policy in the experiments.

本研究讨论了收益管理问题，通过定价商品或服务最大化收益。研究挑战之一是在真实应用中（如航空和零售行业）需求分布未知且随时间变化。为解决此问题，我们首先引入了一种基于典型应用场景的分段泛化收益管理问题，并提出了一种基于后验抽样的计算高效算法。我们推导了这种算法在需求参数可以在时间段之间相关的一般模型中的贝叶斯遗憾上界，同时推导了一种通用算法的遗憾下界。我们的实证研究表明，所提算法优于其他基准算法，并与事后最优策略具有可比性。我们还提出了所提算法的一种启发式修改，该修改在实验中进一步高效地学习定价策略。