International Journal of New Developments in Engineering and Society, 2024, 8(1); doi: 10.25236/IJNDES.2024.080106.
Chengxu Huang1, Zhicheng Zhang1, Qianjin Qu2
1School of Information Science and Engineering, Wuhan University of Science and Technology, Wuhan, 430081, China
2School of Resources and Environmental Engineering, Wuhan University of Science and Technology, Wuhan, 430081, China
Vegetable products possess a limited shelf life and are susceptible to quality deterioration over time, necessitating the establishment of effective replenishment and pricing strategies. This study leverages historical sales and supply-related data from a superstore to conduct a comprehensive analysis. Firstly, a linear regression model is employed to explore the relationship between sales volume and cost pricing for each category across different time periods. Additionally, an ARIMA prediction model is developed to forecast the total daily sales volume with temporal sequence, with model smoothness validation conducted. Building on these analyses, predictions for the total sales volume from July 1 to July 7, 2023 are generated. Subsequently, a nonlinear planning model is constructed, with the objective of maximizing the superstore's revenue by treating the daily sales volume of six types of vegetables as the decision variable and establishing an objective profit function. The replenishment quantity for each category and the decision-making scheme for category unit price are determined through sophisticated problem-solving tools such as spsspro and MATLAB.
ARIMA model, Nonlinear programming model, Replenishment strategy
Chengxu Huang, Zhicheng Zhang, Qianjin Qu. Optimized Decision Making for Vegetable Replenishment and Pricing Based on ARIMA Prediction and Nonlinear Programming Models. International Journal of New Developments in Engineering and Society (2024) Vol.8, Issue 1: 35-41. https://doi.org/10.25236/IJNDES.2024.080106.
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