Research on Optimization of Crop Planting Plans Based on Simulated Annealing and Monte Carlo Simulation

Abstract

In order to help rural agriculture achieve maximum economic benefits and controllable risks, this paper focuses on the optimization of crop planting strategies in a certain village from 2024 to 2030 and constructs a series of models. Firstly, simulated annealing and mixed-integer linear programming algorithms are used to optimize different scenarios of crop overproduction under stable assumptions, and the optimal planting plan is determined. It is found that the strategy of selling overproduced crops at a low price is more beneficial. Then, Monte Carlo simulation and sequential least-squares programming are introduced. By considering the uncertainty of multiple factors and improving the objective function, the planting plan is further optimized. The research results provide a scientific basis for rural agricultural planting decisions, demonstrate the effectiveness of models and algorithms in agricultural planning, and are of great significance for promoting the sustainable development of rural economy.