Research on pricing and replenishment based on optimal programming model

Chaolan Chen; Xi Wu

doi:10.54097/csmsgm04

Authors

Chaolan Chen
Xi Wu

DOI:

https://doi.org/10.54097/csmsgm04

Keywords:

Cluster Analysis, Least Square Method, Time Series, Correlation Analysis.

Abstract

This paper studies the law and correlatoin of different categories of vegetable commodities, and makes the total amount of replenishment and pricing strategy of each vegetable category in the next week, so as to maximize the profit of supermarket. Firstly, the correlatoin between different categories of vegetable products was analyzed based on Pearson correlatoin analysis. The results showed that flower-leaf, cauliflower, pepper had strong correlatoin with edible fungi, while aquatic rhizome and solanaceae had relatively low correlatoin with other categories of vegetables. Then, the monthly and daily sales volume of each category and single vegetable were counted, and the monthly, weekly and daily distribution function of each category and single vegetable was obtained by fitting with Python software using the least square method. The vegetable category cluster analysis based on systematic clustering was used to cluster 6 categories of vegetables, and the 6 categories of vegetables were divided into 3 categories. Then the least square method was used to fit the processed data, and the fitting function expression between the sales volume and the cost plus pricing of each category of vegetables was obtained. The replenishment quantity decision model based on time series was established, and the daily replenishment quantity decision of each vegetable category in the next week was obtained. Finally, according to the correlatoin between sales volume and price, the optimal pricing model based on dynamic market demand is established, and the most reasonable pricing is obtained and the optimal replenishment decision is made to maximize the profit of supermarket.

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References

Cui Yunhao. Research on forecast of fresh vegetable sales based on CNN-PSO- LSTM combination model [D]. Anhui University of Agriculture Xue, 2023.

Wang Zhangzhang. Research and Implementation of Price prediction Model based on Machine Learning [D]. Chang 'a university, 2019.

Li Lian. Research on Cross-variety price transfer in bulk vegetable Market [D]. Huazhong Agricultural University, 2023.

Zhang Jixiao. Study on pro-environment behavior of leaf vegetable growers [D]. Henan Agricultural University, 2023.

Yang Haoxu. Correlatoin analysis between Vegetable Price and sales volume -- A case study of rosella [J]. Food Safety Guide, 2018(21).179- 181.

Mao Lisha. Research on Pricing Strategy and Production and Marketing Model of vegetable Wholesale Market from the perspective of supply chain [D]. Central South University of Forestry and Technology, 2023.

Joint Research Group of Chengdu Development and Reform Commission and School of Economics, Sichuan University, Guo Xiaoman, Zhang Rui et al. Research on Optimization of Quantitative Price forecasting and Application of Agricultural products -- A case study of Major agricultural products in Chengdu [J]. Macroeconomic Research, 2021(06).107-116.

Zhang Yingqi, Tang Heng, Xu Guangcai. Analysis on vegetable consumption preference and its influencing factors of urban and rural residents in Beijing [J]. Agricultural Outlook, 2023, 19(6).127 -134.

Lv Wang. Research on the trend forecasting of fresh vegetable sales based on improved SVM [D]. Anhui Agricultural University, 2021.

Zhang Qianqian. Research on vegetable demand forecasting system based on SVM [D]. Beijing Jiaotong University, 2015.

TOKINAGA S, IKEDA Y. Modeling for Events characterizing Simultaneous Change in Time Series Parameters by Using Coupled Pricing and Parameter Estimation[J]. A - Abstracts of IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences (Japanese Edition), 2014, J97-A (7).