Optimization Of Crop Planting Strategies Based On 0-1 Integer Programming and Simulated Annealing Algorithm

Abstract

This paper uses 0-1 integer programming, simulated annealing algorithm, Monte Carlo simulation and multivariate statistical analysis to investigate the optimization problem of medium- and long-term crop planting strategies, aiming to achieve the synergistic goals of production efficiency improvement and risk control. Firstly, a single-objective model is constructed to maximize the total return, and the optimal solutions for different stagnant scenarios are solved. Secondly, random number generation algorithm and Monte Carlo simulation are introduced to deal with parameter fluctuations and optimize the classification strategy in response to the uncertainty of the external environment. Finally, the correlation analysis and multivariate polynomial regression model are used to compare the trend of returns in different scenarios, and it is found that the annual net return fluctuates non-monotonically. The study provides a quantitative decision-making framework for long-term agricultural planning, helps to realize the synergistic optimization of return and risk, and is of great significance to the sustainable development of rural agriculture.