Research On Optimal Planting Strategy of Crops Based on Nonlinear Programming and Monte Carlo Optimization Simulation

Authors

  • Zijian Zhang College of Aerospace and Civil Engineering, Harbin Engineering University, Harbin, China, 150001
  • Xinyang Liu College of Nuclear Science and Technology, Harbin Engineering University, Harbin, China, 150001
  • Weixuan Shan College of Nuclear Science and Technology, Harbin Engineering University, Harbin, China, 150001
  • Siyu Wang College of Aerospace and Civil Engineering, Harbin Engineering University, Harbin, China,
  • Jiatong Yu College of Aerospace and Civil Engineering, Harbin Engineering University, Harbin, China, 150001
  • Xinying Liu College of Intelligent Science and Engineering, Harbin Engineering University, Harbin, China, 150001
  • Liang Luo College of Computer Science and Technology, Harbin Engineering University, Harbin, China, 150001

DOI:

https://doi.org/10.54097/xr2kee43

Keywords:

Nonlinear programming model, Monte Carlo optimization, Sensitivity analysis, Planting strategy planning, Maximum revenue.

Abstract

This article addresses the issue of crop planting conditions and optimal planting plans, using a nonlinear programming method based on Monte Carlo optimization, visualization processing, sensitivity analysis, and Pearson correlation coefficient analysis. The relationship between the optimal planting plan and various influencing factors such as planting land, crop name, crop type, planting area, and sales unit price is obtained, as well as how to determine the optimal planting plan (i.e. maximum revenue). It is required to find the optimal planting plan for two different sales situations within the limited area of four plots and two greenhouses, as well as various restrictions such as the inability to plant continuously on the same plot and the need to plant different types of crops on different plots. Firstly, preprocess the data to calculate the sales unit price based on the median, and in order to make reasonable use of land resources, multiple types of land can be used on one site; Secondly, using nonlinear programming methods, the restrictive conditions are processed and transformed into mathematical equations, and the rules are summarized and visualized through a lingo processor for analysis; Finally, using the objective function and the optimal planting plan provided by the lingo processor, the maximum profit under two different sales scenarios is obtained, which is denoted as yuan.

Downloads

Download data is not yet available.

References

[1] Leng Gongye, Yang Jianli, Xing Jiaoyang, etc Research on the Opportunities, Problems, and Countermeasures of High-Quality Development of Agriculture in China [J]. China Agricultural Resources and Zoning, 2021, 42 (05): 1-11

[2] Wu Menghan, Wang Yi. Optimization and Adjustment of Multi-objective Planting of Crops in Shache Irrigation District, Xinjiang [J]. CODEN RHEUCE, 2024, 46(1): 120-125, 131.

[3] Tian Man, Ni Wenzhou, Hu Wentao, et al. Discussion on Optimization of Crop Planting Strategies in Mountainous Areas of North China Based on Linear Programming [J]. Southern Agriculture, 25, 19(7): 73-81.

[4] Deng Shuo. Practical Exploration of Monte Carlo Method in Cultivating Students' Computational Thinking [J]. China Modern Educational Equipment, 2024(16): 45-48.

[5] Lin Jiaxun, Xiong Taoli, Dong Xiaoyan, et al. Research on the Application of Monte Carlo Simulation Based on Genetic Algorithm Optimization in Production Decision Optimization [J] Mathematical Modeling and Its Applications, 2025, 14(1): 98-105.

[6] Fang Kaipeng. Research on Location Optimization Algorithm of Mobile Sensor Network Based on Monte Carlo [D] Hunan: Xiangtan University, 2022. DOI: https://doi.org/10.1155/2022/7131099

[7] Han Linshan, Li Xiangyang, Yan Dakao A Brief Analysis of Several Mathematical Methods for Sensitivity Analysis [J]. China Water Transport (Second Half), 2008, (04): 177-178

[8] Andy, Zhang Xinyu. Electricity Bill Default Risk Early Warning Algorithm Based on Pearson Correlation Coefficient and MLP [J]. Electrical Engineering Technology, 2024(20): 59-62.

[9] Peng Shijie, Pan Weihua, Li Zijing, et al. Design of Urban Heat Map System Based on Django and LoRa [J]. Information Recording Materials, 25, 26(2): 67-70,122.

[10] Feng Xingjin. Exploration of the Regular Performance Evaluation Method Based on Pearson Correlation Coefficient [J]. Gansu Educational Research, 2025(2): 114-117.

Downloads

Published

23-12-2025

Issue

Vol. 159 (2025): 2nd International Conference on Mathematical Modeling, Algorithms and Data Processing (MMADP 2025)

Section

Articles

License

Copyright (c) 2025 Highlights in Science, Engineering and Technology

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

How to Cite

Zhang, Z., Liu, X., Shan, W., Wang, S., Yu, J., Liu, X., & Luo, L. (2025). Research On Optimal Planting Strategy of Crops Based on Nonlinear Programming and Monte Carlo Optimization Simulation. Highlights in Science, Engineering and Technology, 159, 202-211. https://doi.org/10.54097/xr2kee43