竞争环境下考虑需求学习的动态定价策略

为了分析在顾客到达率不确定且面临市场竞争的情况下，零售商如何通过需求学习来动态地调整价格，以达到收益最大化的目的，运用博弈理论和动态规划方法建立了多周期随机动态定价优化模型，分析了需求学习下的价格竞争均衡。研究表明，在竞争和需求学习的市场环境下，零售商的价格博弈存在均衡，且多个均衡价格存在时，各零售商的收益与均衡价格成正比。应用算例分析了需求学习的效果，求出了需求学习下零售商竞争的价格均衡，并得出一些管理启示。

Dynamic Pricing Strategy with Demand Learning Based on Competitive Markets

Analyzing why retailers have to adopt dynamic pricing through demand learning to maximize their revenue where there is demand uncertainty of the customer arrival rate and the competitive markets with multiple competing retailers, a dynamic pricing model by dynamic programming and game theory is presented. Based on competitive markets and demand learning background, the existence of Nash Equilibrium is obvious. It is found that the profit of retailers is proportional to the equilibrium price where there are multiple equilibrium prices. Numerical experiments are conducted to analyze the effect of demand learning and calculate the price equilibrium. A managerial insight is derived from numerical experiments.