基于评估优化模型的蔬菜产品自动定价和补货决策

Zhichun Wei
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Secondly, a quarter is taken as a sales cycle of supermarkets, so as to find the proportion of sales volume of a certain category in the same quarter of three years to the total sales volume, and give the distribution law of sales volume of different categories, the results are shown. Considering different periods again, the daily sales volume distribution law is calculated by taking one day as a sales cycle, and the results are shown. Finally, the Pearson grade correlation coefficient is used to judge the relationship between the processing indicators, and the matrix heat map is obtained. According to the two results, it was concluded that there was a significant positive correlation between the sales volume of mosaic and cauliflower vegetables, and a significant negative correlation between the sales volume of nightshade and aquatic root vegetables. 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引用次数: 0

摘要

根据附件中超市的商品信息、历史销售流水明细数据、蔬菜商品批发价格和近期蔬菜商品损耗率,通过对各品类、各单品的数据分析,建立商品自动定价和补货决策模型。利用优化评估算法,制定各品类、各单品的日补货总量和定价策略。为了解决第一个问题,首先对附件 2 和附件 3 原始数据中的异常值进行清理、归一化、特征选择和降维处理。其次,以一个季度作为超市的销售周期,求出三年中同一季度某品类的销售量占总销售量的比例,并给出不同品类销售量的分布规律,结果如图所示。再考虑不同时期,以一天为一个销售周期,计算日销售量分布规律,结果如图所示。最后,利用皮尔逊等级相关系数判断加工指标之间的关系,得到矩阵热图。根据这两个结果,可以得出结论:马赛克蔬菜和菜花蔬菜的销售量之间存在显著的正相关关系,夜交菜和水生根茎类蔬菜的销售量之间存在显著的负相关关系。针对第二个问题,首先考虑总销量与成本定价之间的函数关系,进行相关分析和线性拟合,得到各品类销售价格与上年 7 月各品类销量最大值之间的线性关系,可以说是通过进一步的非线性拟合和优化问题求解、未来一周(2023 年 7 月 1 日至 7 日)各蔬菜品类的日补货总量和定价策略如表 1 和表 2 所示,该超市的收益最大 针对第三个问题,根据已知数据,我们可以分析各数据的数据要求:我们需要知道这一时期各种蔬菜的销售量,我们需要确定每种蔬菜的采购成本,我们需要了解过去的定价策略和应对措施,我们还需要知道 6 月 30 日各种蔬菜的库存量。在此基础上,建立多目标动态编程模型,利用贪心算法求得 7 月 1 日单品补货量,从而求得可销售总数量为 30,并利用问题 2 中拟合的线性方程进一步求解定价策略。针对第四个问题,在现有销售、批发价格和损耗率数据的基础上,为了更好地制定蔬菜产品的补货和定价决策,超市还需要考虑和收集以下 12 个方面的相关数据,以协助规划蔬菜产品的定价和补货决策,如顾客偏好和满意度调查、蔬菜的季节性和供应情况、竞争对手信息、库存成本和储存条件、历史销售数据和趋势分析、顾客流量和购买周期、蔬菜的营养价值和保健功效、社会经济因素、外部环境因素、政策法规因素、技术和创新因素、供应链和物流信息等,以确保决策更加全面准确。其中,主要对历史销售数据和趋势进行分析。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Automated pricing and replenishment decisions for vegetable products based on evaluation optimization models
Based on the commodity information of the supermarket in the annex, the detailed data of historical sales flow, the wholesale price of vegetable commodities and the recent loss rate of vegetable commodities, and through the data analysis of each category and each single product, the automatic pricing and replenishment decision-making model of commodities is established. Use the optimization evaluation algorithm to formulate the total daily replenishment and pricing strategy of each category and each single product. In order to solve the first problem, firstly, the outliers in the original data of Annexes 2 and 3 are cleaned, normalized, feature selected and dimensionally reduced. Secondly, a quarter is taken as a sales cycle of supermarkets, so as to find the proportion of sales volume of a certain category in the same quarter of three years to the total sales volume, and give the distribution law of sales volume of different categories, the results are shown. Considering different periods again, the daily sales volume distribution law is calculated by taking one day as a sales cycle, and the results are shown. Finally, the Pearson grade correlation coefficient is used to judge the relationship between the processing indicators, and the matrix heat map is obtained. According to the two results, it was concluded that there was a significant positive correlation between the sales volume of mosaic and cauliflower vegetables, and a significant negative correlation between the sales volume of nightshade and aquatic root vegetables. In view of the second problem, firstly, considering the functional relationship between the total sales volume and the cost pricing, the correlation analysis and linear fitting were carried out to obtain the linear relationship between the sales price of each category and  the maximum value of the sales volume of each category in July of the previous year can be described as  Through further nonlinear fitting and optimization problem solving, the total daily replenishment volume and pricing strategy of each vegetable category in the coming week (July 1-7, 2023) are shown in Table 1 and Table 2, which makes the supermarket have the largest revenue In response to the third question, based on the known data, we can analyze the data requirements for each data: we need to know the sales volume of various vegetables during this period, we need to determine the purchase cost of each vegetable, we need to understand the past pricing strategy and response, and we need to know the inventory of various vegetables on June 30. On this basis, a multi-objective dynamic programming model is established, and the total number of saleable items is 30 by using the greedy algorithm to obtain the replenishment quantity of single items on July 1, and the pricing strategy is further solved by using the linear equation fitted in problem 2. In response to the fourth problem , on the basis of the existing sales, wholesale price and loss rate data, in order to better formulate the replenishment and pricing decisions of vegetable products, supermarkets also need to consider and collect the following 12 aspects of relevant data to assist in planning the pricing and replenishment decisions of vegetable products, such as: customer preference and satisfaction survey, seasonality and availability of vegetables, competitor information, inventory costs and storage conditions, historical sales data and trend analysis, customer flow and purchase period, nutritional value and health benefits of vegetables, Socio-economic factors, external environmental factors, policy and regulatory factors, technological and innovation factors, and supply chain and logistics information to ensure more comprehensive and accurate decision-making. Among them, the analysis of historical sales data and trends is mainly carried out.
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