Optimization of Vehicle Scheduling and Path Planning for Concrete Batching Plant Based on BP Neural Network and GA Algorithm

In this paper, four multi-objective optimization algorithms, including BP neural network, genetic algorithm, BP-GA algorithm and BP-GA-Pareto algorithm, are proposed for the vehicle scheduling and path planning problems based on the production scheduling and transportation of concrete mixing plant. Through the comparative analysis of the algorithms, it is found that the BP-GA-Pareto algorithm performs the best when the optimization objectives of both vehicle scheduling and path planning are considered comprehensively, and it can better realize the scheduling and path planning of the vehicles of concrete mixing plant to achieve the optimal effect. This study has certain theoretical and practical significance for the optimization of vehicle scheduling and path planning in concrete mixing plants, and also provides some valuable references and ideas for the study of similar problems.