The Optimal Travel Path Model Based on Greedy and Genetic Algorithm

In rural areas, how to make fuller and more efficient use of the existing limited arable land resources, and develop high-quality planting according to local conditions and time is a key move to promote the sustainable development of agriculture and rural areas. This paper carries out an in-depth research on the planting strategy of crops mentioned above, which mainly includes the following aspects: firstly, this paper establishes a linear programming model under constraints and solves it by using the PSO particle swarm optimization algorithm, which integrally takes into account the seasonal requirements and crop planting seasons, and subtly improves the solving efficiency through the particle swarm; then, Monte Carlo simulation is applied to deal with the uncertainty of the data indicators and construct a multi-objective model with a multi-objective model, which is based on a multi-objective model. Then, Monte Carlo simulation was used to deal with the uncertainty of the data indicators, construct a multi-objective linear programming model and extract the Pareto frontier to select the optimal solution; finally, in order to obtain a robust model, a robust planting strategy was searched for in all scenarios to maximise the return under the worst scenario, and in each scenario, parameters such as the sales price volume were adjusted and solved for to select the optimal strategy model.