Research on Cultivation Strategy Based on Robust Optimization Model and Genetic Algorithm

In this paper, an in-depth study is conducted on the problem of cultivation strategy of arable land, and a robust optimization model based on 0-1 integer programming is established. The objective is to optimize cultivation strategies using methods such as controlling variables, categorical discussion, and conceptual model transformation. First, by introducing the demand price elasticity coefficient, the uncertain planting area problem is transformed into a crop and plot selection matching problem. Secondly, for the two strategies of demurrage treatment and selling at reduced price, robust optimization models were established and optimized using a genetic algorithm. The results showed that strategy II was significantly more effective. Finally, based on strategy II, the IGDT robust decision-making model under the risk aversion strategy (RAS) was constructed, considering the uncertainty factor. The results showed that the plot yields fluctuated, but the overall outcome was more stable. The research in this paper provides reference value for actual cropland planting strategies.