An Applied Study Based on Monte Carlo Simulation with a Multi-Objective Optimisation Model

Multi-objective optimisation models are widely used in various fields, but face challenges such as handling complex constraint conditions. This study constructs a single-objective linear programming model and introduces random errors to balance price factors. Considering data fluctuations, a corresponding model is constructed and Monte Carlo simulation is used to handle uncertainty. Based on the correlation of variables and the relationship between crops, a multi-objective optimisation model is constructed, and the weighted method is used to optimise the planting strategy. Through model prediction and analysis, the profits and yields of different crops are obtained, providing strong support for decision optimisation in related fields and helping to improve the performance and application range of multi-objective optimisation models.