Education, Science, Technology, Innovation and Life
Open Access
Sign In

Research on Vegetable Pricing and Replenishment Based on Exponential Smoothing Model Prediction

Download as PDF

DOI: 10.23977/tracam.2024.040102 | Downloads: 1 | Views: 87

Author(s)

Zhenyu Zhou 1, Yuang Zhang 1, Jiaxing Zhao 1

Affiliation(s)

1 School of Mathematics and Statistics, Hubei Engineering University, Xiaogan, 432000, China

Corresponding Author

Zhenyu Zhou

ABSTRACT

Vegetables and fresh food are necessities in daily life. Their quality and price not only affect the interests of businesses, but also have a direct impact on people's livelihood issues. However, this type of commodity has its particularity, its appearance and quality will deteriorate over time, resulting in the need to discount sales or even unable to sell. Therefore, it is a very important task for businesses to seek to establish a good and healthy supply and marketing channel. This article has established an exponential smoothing model to predict the sales volume of its vegetable categories and individual items respectively. It then solves the problem through optimization model algorithms, calculating the optimal daily replenishment total and pricing strategy, thus maximizing the supermarket's revenue.

KEYWORDS

Exponential Smoothing Forecasting, Optimization Model, Time Series, Pearson Correlation Analysis

CITE THIS PAPER

Zhenyu Zhou, Yuang Zhang, Jiaxing Zhao, Research on Vegetable Pricing and Replenishment Based on Exponential Smoothing Model Prediction. Transactions on Computational and Applied Mathematics (2024) Vol. 4: 11-18. DOI: http://dx.doi.org/10.23977/tracam.2024.040102.

REFERENCES

[1] QI An. Research on Pricing of Science and Technology Novelty Searching Services Based on Cost-Plus Pricing Method [J]. Library Research and Work, 2021(10):25-31+24.
[2] Liyuan Ning. Pricing Strategy and Techniques for Green Vegetables [J]. Guangdong Sericulture, 2020, 54(07):111-112.
[3] Zhenhai Cai. Practical Exploration of Anomaly Data Processing and Analysis Based on Python [J]. Computer Knowledge and Technology, 2023, 19(27):62-65
[4] Shuai Xu, Dandan Liu. Electric Load Forecasting Based on Time Series Combination Model [J]. Electronic Design Engineering, 2023, 31(23): 1-6.
[5] Jinwei Wang, Yuhan Shan, Baohai Shan. Data Analysis of Short Track Speed Skating Competition Based on Pearson Correlation Coefficient [J]. Ice and Snow Sports, 2023, 45(04):9-12+17.
[6] Decai Liu, Zhijie Zhou. Research on the demand forecast of aquatic product cold chain logistics based on combined prediction model - taking Jiangsu Province as an example [J]. Logistics Engineering and Management, 2023, 45(10): 4-8.
[7] Dike Sang, Zhihao Ren. Research on Logistics Optimization Based on Maximizing Benefits of SCCY Company [J]. China Logistics and Purchasing, 2023(15):119-120. 
[8] Xiapei Li. Prediction of Agricultural Product Logistics Demand Based on Grey Linear Combination Model [J]. Journal of Beijing Jiaotong University (Social Science Edition), 2017, 16(01): 120-126.
[9] Fang Huang, Rui Yan, Jinzhou Niu. Construction of energy consumption prediction model for cold storage based on time series index smoothing method [J]. Shanghai Energy Conservation, 2023(03):336-341.
[10] Xiaobin Wang, Fujun Lu,Ting Sun, et al. Iterative Computation and Big Data Analysis in Finance [J]. Information and Communication Technology, 2017, 11(04): 47-52. 

Downloads: 150
Visits: 9364

Sponsors, Associates, and Links


All published work is licensed under a Creative Commons Attribution 4.0 International License.

Copyright © 2016 - 2031 Clausius Scientific Press Inc. All Rights Reserved.