Author(s)

ChiHo Thong¹, Jun Ren^2*, ShuHao Liu³

Affiliation(s)

¹School of Economics and Finance, Huaqiao University, Quanzhou 362021, Fujian, China.

²College of Engineering, Huaqiao University, Quanzhou 362021, Fujian, China.

³School of Mathematical Sciences, Huaqiao University, Quanzhou 362021, Fujian, China.

Corresponding Author

Jun Ren

ABSTRACT

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.

KEYWORDS

Crop planting optimization; Crop rotation; Genetic algorithm; Monte carlo simulation; Uncertainty management; Agricultural decision-making

CITE THIS PAPER

ChiHo Thong, Jun Ren, ShuHao Liu. Robust multi-objective crop planning in mountainous northern China via LP–NSGA-II–MONTE carlo. Eurasia Journal of Science and Technology. 2025, 7(5): 1-10. DOI: https://doi.org/10.61784/ejst3105.

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