Qiyuan Zheng
ABSTRACT
This paper mainly uses linear regression analysis, ARIMA model, greedy algorithm and other methods to solve the main problems such as how to accurately predict the sales trend, effectively make the replenishment plan and reasonable price strategy. This study collected various data of vegetable sales in a shopping mall in two years. The box plot is used for data preprocessing, the data is visualized, and the outliers are removed. First, predict the sales volume of each vegetable category from July 1 to 7.The ARIMA model is used to predict the price, and the adjusted grid model is used to predict the price and give the daily replenishment strategy for the next week. Further make sales forecast, replenishment and pricing decision of each vegetable item on July 1. And the data is preprocessed by average value, and the item number without transaction information is screened out. Then the problem is transformed into knapsack problem (a combinatorial optimization NP-complete problem) and solved by dynamic programming. Next, the collected loss rate data are introduced to further clean, screen and modify the previously processed data to establish the correlation between loss rate and shelf life, so as to establish a new model. Finally, the greedy algorithm is used to give the optimal solution, select 33 goods with the highest expected profit, and finally give the pricing strategy on July 1, 2023.
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