An Exploration of Crop Planting Based on Simulated Annealing Algorithm and Mixed Integer Linear Programming

This paper is dedicated to exploring the optimal planting scheme of crops through the comprehensive use of various computer optimization algorithms, and is expected to provide a scientific decision-making basis for crop planting from 2024 to 2030. Firstly, based on Mixed Integer Linear Programming (MILP) and Simulated Annealing Algorithm, this paper analyzes the planting area and benefits of different crops based on the existing plot types and crop cultivation in rural arable land to find the optimal planting plan. Secondly, when facing the uncertainty factors of the future market, this paper quantifies the risk by dynamic programming and genetic algorithm, combining the parameters such as the sales price, planting cost and expected sales volume of the crops, to optimize the planting decision, and considering the substitutability and complementary relationship between the crops. Then, Pearson correlation coefficient and cross-elasticity coefficient are introduced to analyze the correlation between variables such as crop sales price and planting cost, and constrained optimization. Finally, integrating multiple model algorithms, this paper proposes a crop planting strategy that maximizes the comprehensive benefits.