Optimisation of crop planting strategies based on simulated annealing algorithm
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DOI: 10.25236/icetmr.2024.005
Author(s)
Qiang Wang, Jiaheng Huang, Yushan Li, Zihan Yan, Yuxuan Liu, Yumeng Zhang, Siyuan Cao
Corresponding Author
Qiang Wang
Abstract
In this paper, based on the crop sales information, an optimisation model is established for analysis, so as to determine the optimal planting scheme of crops in 2024-2030 to enhance the production efficiency in the field. First, based on the data related to ordinary greenhouses, a table of key variables such as planting scheme, expected sales volume, sales unit price and planting cost is compiled. Assuming that the expected sales volume, planting cost and sales unit price of each crop remain unchanged from 2023 during the period from 2024 to 2030, an optimisation model is constructed to maximise the total return of the crops, and constraints such as plot constraints, planting cost, and crop rotation demand are determined. The crop cropping scenarios in 2023 were used as the initial solution, and the simulated annealing algorithm was used to solve for the optimal cropping scenarios under different market scenarios. In addition, the exponential change model and Monte Carlo simulation are used to construct the forecasting model by taking into account the future sales growth trend of corn and wheat and the fluctuation of mu yield, sales volume, planting cost and sales price of other crops. Secondly, the planting risk brought by uncertainty and climate and market factors is considered comprehensively, and the constructed prediction model is used to add new constraints. Finally, the optimisation model is solved using a dynamic programming algorithm.
Keywords
Simulated annealing algorithm, Monte Carlo simulation, crop planting strategies, optimisation models