Research on Agricultural Planting Planning Optimization Based on Mixed Integer Programming and Genetic Algorithm

Abstract

This study aims to optimize agricultural planting strategies in the mountainous regions of North China, maximizing economic benefits under the constraints of limited and fragmented arable land while addressing challenges such as overproduction, market demand fluctuations, and crop rotation requirements. While China’s rural revitalization strategy accelerates agricultural modernization, mountainous agriculture still faces issues such as fragmented farmland, high production costs, and unstable market demand. Existing research primarily focuses on agricultural resource allocation and planning, but lacks comprehensive solutions to dynamic constraints, including overproduction risks, crop complementarities, and rotation. To address these challenges, this paper proposes an agricultural planting optimization model that integrates mixed integer programming (MIP) with a genetic algorithm (GA). Based on data collection and current state analysis, the model incorporates multiple-variable constraints and is optimized for two scenarios: one where excess production remains unsold, and another where excess produce is sold at a 50% discount. The model is solved, and the results are analyzed to derive the optimal planting strategy. The experimental results show that in the case of overproduction, the discounted sales strategy effectively reduces revenue fluctuations, offering higher revenue stability compared to the unsold scenario. Therefore, it is recommended to adopt a discounted sales strategy in cases of overproduction to stabilize agricultural income and enhance economic benefits. This paper provides theoretical support and practical references for the modernization of agriculture in the mountainous regions of North China.