Research on Planting Optimization Based on Greedy Algorithm and Monte Carlo Simulation

This paper focuses on the application of Monte Carlo simulation methods and greedy algorithms in the field of planting optimization [1]–[3]. A multi-stage stochastic planning model and Monte Carlo simulation method are used to optimize the crop planting scheme of a region from 2024 to 2030 as the research background [4]. First, a multi-stage stochastic planning model is used to achieve dynamic decision-making for each year and maximize crop yield based on the greedy algorithm. Second, Monte Carlo simulation is used to deal with uncertainties such as sales volume, price and cost changes to dynamically adjust the scheme. Finally, combining the substitutability and complementarity of crops, corresponding constraints and incentives are set to optimize the allocation of land resources and improve the overall returns. The study shows that the model has good application prospects in this field and can be extended to other related fields.