Multi-AGV Flexible Manufacturing Cell Scheduling Considering Charging

Because of their flexibility, controllability and convenience, Automated Guided Vehicles (AGV) have gradually gained popularity in intelligent manufacturing because to their adaptability, controllability, and simplicity. We examine the relationship between AGV scheduling tasks, charging thresholds, and power consumption, in order to address the issue of how AGV charging affects the scheduling of flexible manufacturing units with multiple AGVs. Aiming to promote AGVs load balance and reduce AGV charging times while meeting customer demands, we establish a scheduling model with the objective of minimizing the maximum completion time based on process sequence limitations, processing time restrictions, and workpiece transportation constraints. In accordance with the model's characteristics, we code the machine, workpiece, and AGV independently, solve the model using a genetic algorithm, adjust the crossover mutation operator, and incorporate an elite retention strategy to the population initialization process to improve genetic diversity. Calculation examples are used to examine the marginal utility of the number of AGVs and electricity and validate the efficiency and viability of the scheduling model. The results show that the AVGs are effectively scheduled to complete transportation tasks and reduce the charging wait time. The multi-AGV flexible manufacturing cell scheduling can also help decision makers to seek AGVs load balance by simulation, reduce the charging times, and decrease the final completion time of manufacturing unit. In addition, AGV utilization can be maximized when the fleet size of AGV is 20%-40% of the number of workpieces.

Automated summary

Automated Guided Vehicles (AGV) have gained popularity in intelligent manufacturing due to their flexibility, controllability, and convenience. This study examines the relationship between AGV scheduling tasks, charging thresholds, and power consumption in order to address the impact of AGV charging on the scheduling of flexible manufacturing units with multiple AGVs. A scheduling model is established to minimize the maximum completion time, and a genetic algorithm is used to solve the model and improve genetic diversity. The results demonstrate the effectiveness and viability of the scheduling model in optimizing AGV utilization and reducing charging times.