Abstract We consider a retail firm selling a durable product in a volatile market where the demand is price-sensitive and random but its distribution is unknown. The firm dynamically replenishes inventory and adjusts prices over time and learns about the demand distribution. Assuming that the demand model is of the multiplicative form and unmet demand is partially backlogged, we take the empirical Bayesian approach to formulate the problem as a stochastic dynamic program. We first identify a set of regularity conditions on demand models and show that the state-dependent base-stock list-price policy is optimal. We next employ the dimensionality reduction approach to separate the scale factor that captures observed demand information from the optimal profit function, which yields a normalized dynamic program that is more tractable. We also analyze the effect of demand learning on the optimal policy using the system without Bayesian update as a benchmark. We further extend our analysis to the case with unobserved lost sales and the case with additive demand.

中文翻译：

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基于需求学习的动态定价和库存管理：贝叶斯方法

摘要 我们考虑一家零售公司在波动的市场中销售耐用产品，其中需求对价格敏感且随机，但其分布未知。公司动态地补充库存并随着时间的推移调整价格并了解需求分布。假设需求模型是乘法形式并且未满足的需求部分积压，我们采用经验贝叶斯方法将问题表述为随机动态程序。我们首先确定了一组需求模型的规律性条件，并表明依赖于状态的基础库存定价策略是最优的。接下来，我们采用降维方法将捕获观察到的需求信息的比例因子与最优利润函数分开，从而产生更易于处理的归一化动态程序。我们还使用没有贝叶斯更新的系统作为基准来分析需求学习对最优策略的影响。我们进一步将分析扩展到未观察到的销售损失案例和附加需求案例。