Research on Cultivation Planning Based on Multi-Stage Linear Programming Model and Greedy Algorithm

This paper presents a study on cultivation planning. The research primarily involves the construction of a multi-stage linear programming model, integrating the principles of dynamic programming and greedy algorithms to analyze data from previous years for optimal decision-making on an annual basis. Considering the differences in various terrains, the problem is divided into two parts. Furthermore, additional uncertain factors are introduced based on the aforementioned model, such as expected sales growth, yield fluctuations, annual cost escalation, and price variations. Due to the uncertainty of these parameters, the Monte Carlo simulation method is employed to handle these uncertainties. By simulating different scenarios through random sampling, the planning for each year is dynamically adjusted, enhancing the model's adaptability and flexibility to the complex factors of actual production.