Research On Supermarket Replenishment Decision Based on ARIMA Time Series Prediction and Combination Optimization Model
DOI:
https://doi.org/10.54097/fm22z613Keywords:
normalization BIC; ARIMA model; constraint factor; combination optimization model.Abstract
In order to maximize the daily profit of supermarkets, it is necessary to predict the sales volume of each category of vegetables in advance during replenishment to determine the variable cost of replenishment volume. This article uses the ARIMA time series prediction model and a nonlinear programming model with penalty functions to provide a combination optimization decision-making method. Under constraints such as supermarket space constraints and dish richness, it effectively solves the problem of specific daily replenishment total and maximum total profit per day for different products in supermarkets, as well as the problem of maximizing supermarket revenue.
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