Research on Planting Strategy Based on Monte Carlo and Simulated Annealing Algorithms

A crop planting strategy optimization model combining Monte Carlo simulation and simulated annealing algorithm is proposed in the study, aiming to effectively optimize the planting strategy to cope with the complex production environment under uncertain conditions. First, based on a variety of constraints, this paper analyzes in depth the uncertainties involved in the planting process, such as the dynamic changes of various cash crop indicators in the market, by establishing an optimization model with the objective of maximizing benefits. Then, in order to cope with these uncertainties, the original optimization model was dynamically adjusted using Monte Carlo simulation and combined with the simulated annealing algorithm for global optimization calculation. Ultimately, the optimal planting scheme between 2024 and 2030 was determined by this optimization model. This study not only can effectively deal with the complex market dynamics, but also provides a scientific basis for the optimization of planting strategies.