Research on Planting Strategy Based on Simulated Annealing Algorithm and Monte Carlo Modeling

The aim of this study is to optimize the planting strategy of crops based on simulated annealing algorithm and Monte Carlo model in order to enhance the efficiency of resource utilization and sustainable economic development. By analyzing the crop planting data, a crop planting model was established, and the Spearman correlation coefficient was applied to explore the substitutability and complementarity among different crops and the correlation of economic indicators, so as to optimize the planting scheme. The study is divided into three main parts. First, for different plot types, suitable crops and their related information were analyzed to provide a basis for the establishment of objective functions and constraints. Secondly, the implementation results of the simulated annealing algorithm showed that the optimal planting scheme for the future years was obtained after parameter adjustment, reflecting the impact of different cooling coefficients on the yield. Finally, combining with Spearman coefficient analysis, the substitution and complementary relationship between crops was explored, and the greedy strategy was applied to further optimize the planting combinations to ensure a steady increase in returns under controlled risks. The advantage of this study lies in the adoption of a comprehensive model, which can effectively deal with the uncertainty in crop cultivation and enhance the scientific of decision-making. Through the combination of simulation and optimization methods, it provides a feasible planting strategy for economic development, which has important theoretical and practical significance.