Crop Planting Optimization Scheme Based on Linear Programming and Monte Carlo Algorithm

Abstract

Crop planting optimization is an important part of current agricultural development, as it directly influences the productivity and economic viability for farmers. Based on linear programming and the Monte Carlo algorithm, an optimized crop planting strategy is presented to address the problem of uncertain factors such as expected sales volume, planting costs, yield per acre, and fluctuating sales prices. The proposed stochastic simulation model incorporates these uncertainties, using Monte Carlo simulation to generate a wide range of potential market scenarios, reflecting the diverse conditions that farmers may encounter and then applying linear programming to optimize the planting strategy. The results show that in the face of uncertainty, the planting plan should be more diversified, with dynamic adjustments of crop types and planting proportions based on different scenarios, effectively reducing market risks and improving overall returns. This research is significant for the sustainable development of rural economies, as it helps to improve production efficiency and develop organic farming industries.