Author(s)

Yumeng Yang, Chongrun Wang, Liren You, Min Yang, Ruqian Xiao

Corresponding Author

Yumeng Yang

Abstract

This paper investigates a decision-making model for crop planting strategies based on intelligent optimization algorithms. Initially, we collected basic information on arable land, crops, and planting conditions for the year 2023, and performed data preprocessing to ensure the accuracy and reliability of the results. We then established a decision-making model aimed at maximizing total revenue and used MATLAB to solve the optimization model, obtaining the optimal planting plans for scenarios where the excess expected sales volume is either unsold or sold at 50% of the 2023 price. The article also constructs a linear programming model considering the overstocking issue due to excess sales beyond expectations and proposes a new objective function. Additionally, we incorporated uncertainties such as sales growth rates, yields, planting costs, and sales prices, and developed a robust optimization model. By analyzing the substitutability and complementarity of crops, we established a multi-objective optimization model and solved the optimal planting strategy using genetic algorithms. Finally, we verified the rationality of the model by comparing the planting revenue before and after optimization, and found that the revenue increased after optimization.

Keywords

Crop Planting Strategy, Intelligent Optimization Algorithms, Linear Programming, Robust Optimization, Genetic Algorithms