Research on Crop Planting Decision Model Based on Linear Programming and Genetic Algorithm

Huiyang Jiang; Yihan Tian

doi:10.54097/nqvf1x13

Authors

Huiyang Jiang
Yihan Tian

DOI:

https://doi.org/10.54097/nqvf1x13

Keywords:

Linear Programming, Improved Genetic Algorithm, NSGA-II, Orthogonal Experiment.

Abstract

This study presents an optimization of crop planting strategies for a rural area in the mountainous region of North China, covering the period from 2024 to 2030, with the objective of maximizing profits amid resource constraints and market volatility. We employed a linear programming model to identify profit-maximizing planting strategies while incorporating multiple constraints. Utilizing an enhanced genetic algorithm, the model projected maximum profits of 53 million and 58 million yuan under different scenarios. Furthermore, we developed a bi-objective robust optimization model informed by orthogonal experimental design, utilizing an improved NSGA-II algorithm to address market uncertainties. This approach yielded average profits of 54.4 million and 61 million yuan, with minimum profit guarantees of 54.25 million and 59.5 million yuan in adverse conditions, effectively managing planting risks. The findings provide a framework for optimizing crop strategies in mountainous regions of North China, promoting economic growth and sustainable agricultural practices while enhancing decision-making and market resilience.

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