Clean Energy Self-Consistent Systems for Automated Guided Vehicle (AGV) Logistics Scheduling in Automated Ports

To enhance the logistics scheduling efficiency of automated guided vehicles (AGVs) in automated ports and achieve the orderly charging and battery swapping of AGVs as well as self-sufficient clean energy, this paper proposes an integrated optimization method. The method first utilizes graph theory to construct a theoretical model that includes AGVs, the port road network, and charging and battery-swapping stations, in order to analyze the optimal logistics scheduling and charging and swapping strategies. Subsequently, for the multi-objective optimization problems in AGV logistics scheduling and charging and swapping, a fast solution method based on the immune optimization algorithm is proposed, with scheduling time and the self-sufficiency rate of clean energy for port AGVs as the constraint conditions. Finally, the effectiveness of the proposed model and algorithm is verified through a simulation scenario. The results show that in the simulated port logistics scenario, after optimization, the total operation time of AGVs is significantly reduced. Compared with the cases that only consider scheduling time, the charging strategy, or wind and solar output, the average clean energy self-sufficiency rate under the proposed strategy increased by 82.7%, 27.5%, and 53.9%, respectively. In addition, as the weight of the self-sufficiency rate increases, both the total driving time and the total clean energy self-sufficiency rate of AGVs show an upward trend and are approximately linearly related. Within the specified maximum scheduling time, the actual scheduling time and self-sufficiency rate can be flexibly coordinated, with significant carbon reduction benefits.