Performance of an LP-Based Control for Revenue Management with Unknown Demand Parameters
Performance of an LP-Based Control for Revenue Management with Unknown Demand Parameters
复制标题
未知需求参数下基于 LP 的收益管理控制的性能
作者：
Stefanus Jasin
我们考虑一个标准的网络收益管理（RM）问题，并研究了性能的线性规划（LP）为基础的控制，概率分配控制（PAC），在存在未知的需求参数。我们发现，频繁的重新优化PAC没有重新估计足以缩小估计误差对收入损失的渐近影响。如果，除了重新优化，我们也经常重新估计的参数，我们证明了PAC的性能在未知参数设置几乎是一样好的PAC的性能在已知参数设置。我们的数值实验表明，在大多数情况下，PAC产生的订单收入提高0.5%-1.5%相对于基于LP的预订限制和投标价格。鉴于RM行业的利润率很小，例如航空业（约2%），这种改善水平很容易转化为利润的显著增加。
We consider a standard network revenue management (RM) problem and study the performance of a linear program (LP)-based control, the Probabilistic Allocation Control (PAC), in the presence of unknown demand parameters. We show that frequent re-optimizations of PAC without re-estimation suffice to shrink the asymptotic impact of estimation error on revenue loss. If, in addition to re-optimizations, we also frequently re-estimate the parameters, we prove that the performance of PAC in the unknown parameters setting is almost as good as the performance of PAC in the known parameters setting. Our numerical experiments show that PAC yields a revenue improvement of order 0.5%–1.5% relative to LP-based Booking Limit and Bid Price in most cases. Given the small margin in RM industries, such as the airline industry (about 2%), this level of improvement can easily translate into a significant increase in profit.