程式化事实
收益管理
动态定价
马尔可夫决策过程
收入
计算机科学
部分可观测马尔可夫决策过程
启发式
背景(考古学)
运筹学
订单(交换)
微观经济学
马尔可夫过程
过程(计算)
数学优化
马尔可夫链
经济
马尔可夫模型
数学
人工智能
机器学习
古生物学
宏观经济学
会计
操作系统
生物
统计
财务
作者
Yossi Aviv,Amit Pazgal
出处
期刊:Management Science
[Institute for Operations Research and the Management Sciences]
日期:2005-09-01
卷期号:51 (9): 1400-1416
被引量:181
标识
DOI:10.1287/mnsc.1050.0393
摘要
In this paper, we develop a stylized partially observed Markov decision process (POMDP) framework to study a dynamic pricing problem faced by sellers of fashion-like goods. We consider a retailer that plans to sell a given stock of items during a finite sales season. The objective of the retailer is to dynamically price the product in a way that maximizes expected revenues. Our model brings together various types of uncertainties about the demand, some of which are resolvable through sales observations. We develop a rigorous upper bound for the seller’s optimal dynamic decision problem and use it to propose an active-learning heuristic pricing policy. We conduct a numerical study to test the performance of four different heuristic dynamic pricing policies in order to gain insight into several important managerial questions that arise in the context of revenue management.
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