A Task Scheduling Method for Multi-Load Agvs Considering Battery Constraints

Abstract: In the context of the growing smart manufacturing, task scheduling systems based on multi-load automated guided vehicles are attracting attention. Optimizing the time for AGVs to perform their tasks and balancing the route time cost of individual AGVs can effectively improve efficiency. In this paper, an improved NSGA-II algorithm considering multi-load AGVs is proposed. With the optimization objectives of minimizing the route time cost and minimizing the difference in the route time cost of each AGV. The proposed algorithm includes a chromosome structure containing charging information, efficient heuristic operators for initial population generation, population crossover methods, and mutation operators. Computational experimental results show that the improved NSGA-II algorithm has better optimization ability and iteration efficiency compared to the basic NSGA-II algorithm and similar multi-objective algorithms. The improved NSGA-II algorithm based on the E-ADARP model can efficiently plan reasonable delivery plans and effectively improve the system efficiency