Author(s)

Mingjia Liu ¹, Jiaxing Yang ¹, Zelong Huang ²

Affiliation(s)

¹ Department of Mathematics, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China
² Department of Computer Science and Artificial Intelligence, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China

Corresponding Author

Jiaxing Yang

ABSTRACT

For vegetable sellers, appropriate purchasing and pricing strategies can generate greater profits. This paper explores the periodicity of sales volume for different categories of vegetables using wavelet analysis, and analyzes the differences between different categories through t-tests. Specifically. (1) representative daily average sales volumes of individual products are selected for Spearman correlation analysis, revealing weak correlations among different products within the same category, but strong correlations among certain products from different categories; (2) the relationship between sales volume and selling price, cost price data for each category is analyzed, revealing an exponential relationship between sales volume and selling price/cost price. Based on the principle of market lag, a single-objective optimization model is established to maximize profits; (3) the model is solved using differential evolution algorithm to obtain the optimal pricing and purchasing quantities for each category of vegetables. After implementing the strategies, the benefits have increased by approximately 167% (aquatic root and stem vegetables), 55% (flower and leaf vegetables), 21% (flower vegetables), 81% (eggplants), 40% (peppers), and 38% (edible fungi) over the past 30 days. Significant profit growth is observed in all categories, indicating the rationality of decision-making. 

KEYWORDS

Wavelet analysis; market lag; Differential evolution algorithm; Single-objective optimization; Vegetable pricing

CITE THIS PAPER

Mingjia Liu, Jiaxing Yang, Zelong Huang, Research and Analysis of Vegetable Pricing Strategy Model Based on Single Objective Optimization Algorithm. Information Systems and Economics (2023) Vol. 4: 116-126. DOI: http://dx.doi.org/10.23977/infse.2023.041016.

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