Academic Journal of Computing & Information Science, 2023, 6(11); doi: 10.25236/AJCIS.2023.061117.
Aoyuan Zhang1, Yi Tian2, Qiang Dai3
1College of Life Sciences, Northwest A&F University, Yangling, China
2College of Information Engineering, Northwest A&F University, Yangling, China
3College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, China
This paper stands for the perspective of vegetable superstores and provides an in-depth analysis of historical operational data. Through data preprocessing, data visualization and Spearman's correlation analysis, the sales distribution patterns of vegetable categories and individual items were revealed, and the vegetable replenishment and pricing strategies for the coming week and day were further formulated by using nonlinear fitting and seasonal ARIMA models. The results of the study showed that leafy and flowering vegetables contributed the most to the sales of the superstore, followed by chili peppers and edible mushrooms; meanwhile, the optimal replenishment and pricing strategies for each individual item were obtained through genetic algorithms. These analyses provide decision support for the superstore and help maximize revenue while meeting market demand.
Vegetable Superstores, Data Analysis, Time Series Modeling, Pricing Strategies
Aoyuan Zhang, Yi Tian, Qiang Dai. Supermarket Business Strategy Based on Time Series Forecasting Model and Genetic Algorithm. Academic Journal of Computing & Information Science (2023), Vol. 6, Issue 11: 133-143. https://doi.org/10.25236/AJCIS.2023.061117.
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