Blind Network Revenue Management

被引:86
作者
Besbes, Omar [1 ]
Zeevi, Assaf [1 ]
机构
[1] Columbia Univ, Grad Sch Business, New York, NY 10027 USA
基金
美国国家科学基金会;
关键词
ALGORITHM; INVENTORY;
D O I
10.1287/opre.1120.1103
中图分类号
C93 [管理学];
学科分类号
12 ; 1201 ; 1202 ; 120202 ;
摘要
We consider a general class of network revenue management problems, where mean demand at each point in time is determined by a vector of prices, and the objective is to dynamically adjust these prices so as to maximize expected revenues over a finite sales horizon. A salient feature of our problem is that the decision maker can only observe realized demand over time but does not know the underlying demand function that maps prices into instantaneous demand rate. We introduce a family of "blind" pricing policies that are designed to balance trade-offs between exploration (demand learning) and exploitation (pricing to optimize revenues). We derive bounds on the revenue loss incurred by said policies in comparison to the optimal dynamic pricing policy that knows the demand function a priori, and we prove that asymptotically, as the volume of sales increases, this gap shrinks to zero.
引用
收藏
页码:1537 / 1550
页数:14
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