Research on Agricultural Crop Planting Strategy Based on Robust Optimization Model: Addressing Uncertainty and Risk

This paper aims to solve the complex problems in agricultural planting through innovative methods, optimize the planting strategy, improve the economic benefits of agricultural production and its risk resistance ability, and contribute to the sustainable development of agriculture. To this end, an optimization model for crop planting strategies using an improved simulated annealing algorithm is proposed. Moreover, a hybrid optimization framework combining linear programming and dynamic simulated annealing is constructed to address the non-convex agricultural planting problems. Meanwhile, robustness constraints, including dynamic temperature decay, acceptance probability adaptation, and iteration regulation, are introduced to enhance the adaptability and reliability of the model. The experimental results based on the data in 2024 demonstrate that, under the uncertainties of sales volume (±5%) and planting area (±10%), the standard deviation of returns is reduced by 37%, which significantly improves the risk resistance ability of agricultural production. This provides strong support for the sustainable development of agriculture and also offers a new and effective approach for strategy formulation and risk management in agricultural production practices.