ROBUST MULTI-OBJECTIVE CROP PLANNING IN MOUNTAINOUS NORTHERN CHINA VIA LP–NSGA-II–MONTE CARLO

This paper addresses the optimization of crop planting strategies for a mountainous rural village in northern China, characterized by diverse land resources and strict crop rotation requirements. A comprehensive mathematical modeling framework is developed, incorporating linear programming, genetic algorithms (NSGA-II), and Monte Carlo simulation to address multi-objective optimization and uncertainty in agricultural production. The study considers multiple practical constraints, including land type suitability, crop rotation, legume planting frequency, and limitations on greenhouse cultivation. The results reveal that the proposed models can effectively generate robust, adaptive cropping plans that maximize economic returns while reducing planting risks under both stable and fluctuating market conditions. The integration of Monte Carlo simulation enables the model to account for yield, price, and cost uncertainty, providing decision-makers with reliable strategies for risk management. The findings offer theoretical and practical guidance for agricultural planners and farmers, and the modeling framework has strong scalability for application in diverse environments.