Optimization of Crop Planting Strategies Based on Monte Carlo Simulation and Greedy Algorithm
DOI:
https://doi.org/10.54097/5as5pd83Keywords:
Goal Programming Model, Monte Carlo Simulation, Time Series Regression, Greedy Algorithm, Pearson Correlation CoefficientAbstract
Based on Monte Carlo simulation and greedy algorithm, this paper explores the optimization scheme of crop planting under different conditions. First, for the planting scheme under stable market conditions, this paper establishes an objective planning model to optimize the planting plan from 2024 to 2030 by maximizing the annual planting revenue and combining the constraints of various economic indicators. Further, considering that part of the crop may be stagnant or sold at a discounted price, this paper explores the impacts of different sales scenarios on the planting strategy. Then, for the planting strategy under uncertainty, this paper uses Monte Carlo simulation and time series regression analysis to predict various economic parameters of crops, and based on this prediction result, the greedy algorithm is applied to optimize the planting plan. Finally, the planting scheme is further optimized by considering the complementarity and substitutability between crops and combining the correlation between the indicators of crops.
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