Research on Optimization of Crop Planting Scheme Based on Simulated Annealing Algorithm

In response to the challenges of sustainable agricultural development posed by population growth and food demand, this study focuses on a rural area in North China, utilizing mathematical models and optimization algorithms for planting decision optimization. After preprocessing and visualizing cultivation data, two optimization models were developed: The first model maximizes crop profits by treating excess production as unsold inventory constraints while considering planting quantities, land suitability, and crop rotation restrictions, resulting in an optimal planting scheme for 2024-2030 with a total seven-year revenue of 40.126 billion yuan. The second model incorporates a 50% price reduction strategy for excess production, solved using a simulated annealing algorithm, achieving an optimized plan with a total revenue of 41.834 billion yuan over seven years. Comparative analysis demonstrates that the model employing the price reduction strategy exhibits greater practical applicability, robustness, and utility.