Crop Planting Strategy Formulation based on Optimization Algorithm and Decision Matrix

The optimal decision-making model for crop planting is a crucial research topic for maximizing the profits of planting companies and achieving efficient agricultural production. This study focuses on two scenarios: overproduction waste and overproduction half-price sales. By combining greedy algorithms, genetic algorithms, and decision matrices, we simplify the calculation process and develop reasonable crop planting strategies for each scenario. To address the uncertainty in crop planting, we establish an uncertain risk optimization decision model, using entropy weight-TOPSIS to quantify the risk. We then use genetic algorithms and simulated annealing algorithms to solve the fuzzy optimal strategy and compare the characteristics of the two algorithms. The results show that the simulated annealing algorithm exhibits faster convergence speed and higher solution stability, making it suitable for handling uncertain optimization problems.