蔬菜类商品的自动定价与补货决策问题的研究

Research on Automatic Pricing and Replenishment Decision-Making of Vegetable Commodities

摘要: 蔬菜类商品的销售与时间息息相关，于是合理的销售组合对于蔬菜商品来说非常重要。本文为了得到合理的蔬菜商品的自动定价与补货模型，根据一批蔬菜商品销售数据中各商品的历史销售和需求情况，对接下来的蔬菜类商品销售情况进行预测和分析，基于Apriori算法进行关联规则挖掘，筛选出了符合蔬菜销售季节性要求、此时间段内可以销售的蔬菜商品，接着用ARIMA模型进行时间序列预测。在此基础上通过需求价格弹性模型建立了定价模型，得到了需求量变化和价格变化之间的关系。最后用粒子群寻优算法进行寻优求解，寻找收益最大值，得到了接下来一周在售的所有蔬菜的补货计划以及定价方案并且通过计算得到了最优方案收益。通过综合论证影响蔬菜商品的不同的因素，得出了竞争关系、用户反馈与历史需求等数据对商超蔬菜商品补货和定价决策的影响更大并分析了对其具体影响有哪些，由此可以更加合理和精准的制定补货和定价策略。

Abstract: The sales of vegetable products are closely related to time, so the right sales mix is very important for vegetable products. In order to obtain a reasonable automatic pricing and replenishment model of vegetable commodities, this paper predicts and analyzes the sales of vegetable commodities ac-cording to the historical sales and demand of each commodity in a batch of vegetable commodity sales data, and performs correlation rule mining based on Apriori algorithm, screens out the vege-table commodities that meet the seasonal requirements of vegetable sales and can be sold within this time period, and then uses the ARIMA model to make time series forecasting. On this basis, the pricing model is established through the demand price elasticity model, and the relationship be-tween demand change and price change is obtained. Finally, the particle swarm optimization algo-rithm is used to find the maximum return, and the replenishment plan and pricing scheme of all vegetables sold in the following week are obtained, and the optimal scheme benefit is obtained through calculation. Through the comprehensive demonstration of different factors affecting vege-table commodities, it is concluded that data such as competitive relationship, user feedback and historical demand have a greater impact on the replenishment and pricing decisions of vegetable commodities in supermarkets, and the specific impact on them is analyzed. This allows for more ra-tional and accurate replenishment and pricing strategies.