基于需求学习的多阶段动态折扣和库存优化研究——以服装销售为例

Multi-Stage Dynamic Discount and Inventory Optimization Model Based on Demand Learning: Taking Garment Sales as an Example

摘要: 本文针对需求模型不确定性问题，从需求学习的角度，基于已知的先验需求模型，应用数据同化技术对需求模型中的参数进行不断学习，利用各个观测时刻的数据对模型的状态值需求参数进行优化，建立了基于需求学习的多阶段动态折扣和库存模型，制定出合理的折扣策略满足商家的销售目标。通过实证研究表明，本文提出的模型可以在需求不确定性下通过制定合理的折扣，有效降低商家的库存水平，为打折促销提供科学的依据，并通过期初库存量和初始价格的灵敏度分析，可以找到最优期初库存量和初始价格，为商家的订货量和初始定价的确定提供有力支持，提高了对动态系统的控制水平。

Abstract: Aiming at the uncertainties of demand model, based on the known prior demand model, data assimilation technology is applied to continuously learn the parameters of demand model, and the state-valued demand parameters of the model are optimized by using the data of each observation time. A multi-stage discount and inventory model based on demand learning is established to formulate a reasonable discount strategy to meet the business sales objectives. Empirical research shows that the proposed model can effectively reduce the inventory level of merchants by making reasonable discounts under demand uncertainty, and provide scientific basis for discount promotion. Through sensitivity analysis of initial inventory and initial price, the optimal initial inventory and initial price can be found. It provides strong support for the determination of order quantity and initial pricing, and improves the control level of dynamic system.