Research on Optimization of Crop Planting Scheme Based on Simulated Annealing Algorithm

Hao Zhang; Shengbing Xu

doi:10.54097/bwzqqf11

Authors

Hao Zhang
Shengbing Xu

DOI:

https://doi.org/10.54097/bwzqqf11

Keywords:

Simulated Annealing Algorithm, Crop Planting Schemes, Optimization Model, Sustainable Development.

Abstract

In response to the challenges of sustainable agricultural development posed by population growth and food demand, this study focuses on a rural area in North China, utilizing mathematical models and optimization algorithms for planting decision optimization. After preprocessing and visualizing cultivation data, two optimization models were developed: The first model maximizes crop profits by treating excess production as unsold inventory constraints while considering planting quantities, land suitability, and crop rotation restrictions, resulting in an optimal planting scheme for 2024-2030 with a total seven-year revenue of 40.126 billion yuan. The second model incorporates a 50% price reduction strategy for excess production, solved using a simulated annealing algorithm, achieving an optimized plan with a total revenue of 41.834 billion yuan over seven years. Comparative analysis demonstrates that the model employing the price reduction strategy exhibits greater practical applicability, robustness, and utility.

Downloads

Download data is not yet available.

References

[1] Du Yongzhi, Zhao Chunhui, Feng Yanzhi. Exploration of crop planting management technology [J]. Intelligent Agriculture Guide, 2022, 2 (11): 67 - 69.

[2] Wang Qian. Problems and countermeasures of the current situation of crop planting in Zhengzhou City [J]. Southern Agriculture, 2024, 18 (12): 57 - 59.

[3] Deng Linlin, Wang Zhikai, Han Xiaofei. Sustainable agricultural development based on the optimization model of agricultural planting structure--taking Hetao Irrigation District as an example [J]. Bohai Economic Outlook, 2022, (02): 44 - 46.

[4] Hu G, Ren Z, Chen J, et al. Using the MSFNet model to explore the temporal and spatial evolution of crop planting area and increase its contribution to the application of UAV remote sensing [J]. Drones, 2024, 8 (9): 432 - 432.

[5] Wu L, Tian J, Liu Y, et al. multi-objective planting structure optimization in an irrigation area using a grey wolf optimization algorithm [J]. Water, 2024, 16 (16): 2297 - 2297.

[6] Jiang S, Yu J, Li S, et al. Evolution of crop planting structure in traditional agricultural areas and its influence factors: a case study in Alar Reclamation [J]. Agronomy, 2024.

[7] Li X, Jiang C, Wang Y, et al. Moving forward from escaping the poverty trap in China's greenest regions: examining four decades of socioecological evolution to re-orient sustainable development policies [J]. Applied Geography, 2024.

[8] Huang Y, Liu Z. Improving Northeast China's soybean and maize planting structure through subsidy optimization considering climate change and comparative economic benefit [J]. Land Use Policy, 2024.

[9] Adamo T, Colizzi L, Dimauro G, et al. Crop planting layout optimization in sustainable agriculture: a constraint programming approach [J]. Computers and Electronics in Agriculture, 2024.

[10] Ma S, Ritsema J C, Wang S. Achieving sustainable crop management: a holistic approach to crop competitiveness assessment and structure optimization with dual natural-social environmental impacts [J]. Agricultural Systems, 2024.

[11] Zhang Jiulong, Wang Xiaofeng, Lu Lei, et al. Improved simulated annealing algorithm for solving the rule satisfiability problem [J]. Modern Electronic Technology, 2022, 45 (5): 122 - 128.

[12] Wu Yuxiang, Wang Xiaofeng, Yu Zhuo, et al. A fast simulated annealing algorithm for solving the Max-SAT problem [J]. Journal of Zhengzhou University (Science Edition), 2023, 55 (4): 46 - 53.