Optimization of Planting Strategies Based on Dynamic Programming and Monte Carlo Simulation

Abstract

This study uses a combination of dynamic programming and Monte Carlo simulation with the aim of optimizing crop cultivation strategies in a rural village in a mountainous area. First, the optimal cropping scenarios for the years 2024 to 2030 were determined using linear programming for stable economic indicators, and the returns under different scenarios (stagnant sales and discounted sales) were calculated. Secondly, the increasing trend of expected crop sales as well as the volatility were considered, and the forecast of total returns for the next seven years was obtained through dynamic programming and Monte Carlo simulation. Finally, the study introduced the substitutability and complementarity among crops, as well as the correlation between various economic indicators, modified the objective function, and further optimized the planting strategy. The planting scheme proposed through this study not only maximizes economic benefits and minimizes risks in crop cultivation, but also the results of the study will provide a reference for crop cultivation in other regions.