部分信息下联合鲁棒定价、订货决策的报童模型

信息完全下的经典报童模型是单周期库存管理的核心控制方法，联合定价、订货决策是提高报童模型运作效率的主要手段. Scarf创造性地构建了部分信息下的鲁棒报童模型，但鲁棒联合定价、订货问题由于其困难程度，长期困扰着运作管理学者. 本论文在“做和型”随机价格需求函数条件下部分地解决了该问题. 在报童仅获知不确定需求的均值与方差的假设下，我们建立了Worst-case型的鲁棒联合定价、订货模型，并在适当的条件下给出了联合决策的闭环最优解. 数值实验证实了联合的鲁棒定价与订货决策较大幅度地改善了Scarf鲁棒订货模型的运作效率. 该模型能较好地适应于新产品供应链等信息缺失下的企业运作管理.

Jointly robust pricing and order decision newsvendor model under the limited information

The classical newsvendor model under complete information is the crucial controlling method of single period inventory management, the jointly pricing and ordering decisions are the primary means to improve the operational efficiency of newsvendor model. Scarf first addressed the robust newsvendor problem under partial information, but the robust jointly pricing and ordering problem, due to its difficulty, has long plagued operation and management scholars. We establish the worst-case robust jointly pricing and ordering model where only the mean and the variance of the demand are known, and provide a closed-loop optimal solution of the joint decision making under appropriate conditions. Numerical experiments confirm that the jointly pricing and ordering decisions can improve the operational efficiency of Scarf's robust ordering model greatly. Our model is suitable to new product operation management, the characteristic of which is the operation environment under limited information.