Research on Multi-load AGV Scheduling Based on Improved Genetic Algorithm

Abstract

In response to the problems of heavy workload and long handling time caused by manual forklift handling of parts in automobile manufacturing enterprises producing large parts, introduction of intelligent material handling robots, or "AGVs", to achieve intelligent logistics transportation in factories. Firstly, the shortest distance between each AGV and each work station is taken as the objective function, secondly, the AGV scheduling model is established whereas keeping in mind the double constraints of distribution time and load capacity, and finally, the improved genetic algorithm is used to solve and confirm the scheduling results. The results show that, when compared to the standard genetic algorithm, the improved genetic algorithm reduces total distance travelled by 20.19%, increases AGV loading rate by 23.75%, and meets the time window requirements, making the material distribution more timely.