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ORIGINAL RESEARCH article

Front. Sustain. Food Syst.
Sec. Agricultural and Food Economics
Volume 8 - 2024 | doi: 10.3389/fsufs.2024.1400219

Research on Vegetable Sales and Replenishment Strategies Based on Genetic Algorithm and ARIMA Modeling

Provisionally accepted
Na-Na Cai Na-Na Cai 1Qi Han Qi Han 2*Yi-Yuan Yin Yi-Yuan Yin 1
  • 1 Shandong University of Science and Technology, Qingdao, Shandong Province, China
  • 2 School of Statistics, Shandong University of Finance and Economics, Jinan, China

The final, formatted version of the article will be published soon.

    Vegetable products cannot be sold after staying overnight, so merchants will replenish stocks based on the historical sales and demand of these products. This paper analyzes and forecasts vegetable products using genetic algorithms and ARIMA models. First, the data is preprocessed using the quartile method, with median replacement for outliers. A visual grey relational model is established to explore the sales correlation between different types and individual items of vegetables, and the Spearman correlation coefficient is used to validate the model, showing a high degree of consistency between the data and actual values. Secondly, the data is processed according to time series, and ARIMA is used to forecast the sales volume for a week. Finally, the predicted sales volume and optimal selling price are taken as indicators, and based on the genetic algorithm, the predicted revenue for each category and the total revenue are forecasted to determine the optimal daily replenishment quantity and pricing strategy for a week, and the total revenue is calculated. Sensitivity analysis is applied, and the results show that the GA+ARIMA combined model has high computational accuracy and precision, is suitable for scenarios where revenue is calculated, has strong stability, and excellent anti-interference capabilities. This study will assist merchants in making the best decisions for vegetable replenishment.

    Keywords: Quartile method, vegetable replenishment, Gray correlation analysis, Genetic Algorithm, ARIMA model

    Received: 13 Mar 2024; Accepted: 28 Oct 2024.

    Copyright: © 2024 Cai, Han and Yin. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Qi Han, School of Statistics, Shandong University of Finance and Economics, Jinan, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.