Minimum Risk Decision Making Problem Based on Single-Objective Stochastic Optimization Model and Genetic Algorithm

This paper proposes a planting strategy optimization method based on a single-objective stochastic optimization model and a genetic algorithm, aiming at solving the minimum risk decision-making problem in agricultural production. The article firstly introduces the importance of decision-making in uncertain environments, especially in the field of agriculture, where decision-makers need to find a balance between risk and return. By building a single-objective planning model, this article considers factors such as planting costs, market demand, crop yields and selling prices with the objective of maximizing annual profits. The model introduces 0-1 decision variables and continuous numerical variables, as well as a series of constraints, including the no recropping constraint, the legume crop planting constraint, and the crop planting concentration constraint. To deal with uncertainty, this paper employs a stochastic simulation approach and controls risk through conditional value-at-risk (CVaR). Using genetic algorithm to solve the model, this paper demonstrates the predicted economic benefits over the period 2024-2030 and compares the results with those of the improved model. The results show that the proposed optimization model is able to provide effective planting strategies taking into account uncertainty and risk control, and thus has practical implications in terms of economic benefits.