Author(s)

Cong Cai ¹

Affiliation(s)

¹ School of Science, China University of Petroleum (East China), Qingdao, China

Corresponding Author

Cong Cai

ABSTRACT

This paper solves the problem of how to plan the crop planting programme from 2024 to 2030 in order to get the highest total return, using dynamic planning as the basic method, applying the idea of greedy algorithm, each year based on the prediction of the previous year to formulate the decision of the current year, and constructing and solving the multi-stage planning model for the two treatments of the stagnant portion of the optimal decision-making for the period of 2024 to 2030 respectively. The optimal decision for 2024 to 2030 was obtained. At the same time, various potential planting risks such as past sales experience, expected sales volume of various crops, mu yield, planting cost and uncertainty of sales price were considered, and each parameter may be different at different times. In view of the above uncertainties, this paper adopts Monte Carlo algorithm and probabilistic statistical method to simulate the problem in different scenarios, and obtains the expected value of the problem through a large number of random samples sampling to enhance the planting programme's risk-resistant ability.

KEYWORDS

Multi-objective planning, Monte Carlo, greedy algorithms, optimisation strategies

CITE THIS PAPER

Cong Cai, Optimal crop planting strategies based on multi-objective planning and Monte Carlo algorithm. Information Systems and Economics (2024) Vol. 5: 1-9. DOI: http://dx.doi.org/10.23977/infse.2024.050501.

REFERENCES

[1] WU Menghan, WANG Yi. Optimal adjustment of multi-objective planting of crops in Shache Irrigation District of Xinjiang [J]. People's Yellow River,2024,46(01):120-125+131.
[2] Su Xiaofeng. Research on crop planting decision-making model based on big data analysis technology[J]. Heilongjiang Grain,2023,(10):88-90.
[3] Pang Qinqing. Research on third party payment enterprise value assessment based on Monte Carlo simulation[D]. Chongqing University of Technology,2024.
[4] Han Hongmei. Project cost prediction model based on statistical simulation algorithm[J]. China Construction Metal Structure,2024,23(08):38-40. 
[5] Zhang Xinyu. Digitally assisted enterprise value assessment based on improved DCF model of Monte Carlo simulation method[D]. Yunnan University of Finance and Economics, 2024.

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