Energy-efficient path planning for a multi-load automated guided vehicle executing multiple transport tasks in a manufacturing workshop environment

Automated guided vehicles (AGVs) are typical intelligent logistics equipment, and path planning plays a significant role in the efficient use of AGVs. To better utilize multi-load AGVs and enhance the sustainability of the logistics process, an energy-efficient path planning model is formulated for a multi-load AGV executing multiple transport tasks in a manufacturing workshop environment, with transport distance and energy consumption (EC) serving as optimization objectives. Furthermore, a two-stage approach is proposed to solve it. In the first stage, the optimal energy-efficient paths connecting any two different nodes are acquired based on the workshop transport network expressed as a topological map. Afterward, the non-dominated sorting genetic algorithm-II is adopted in the second stage to determine the optimal execution sequence of pickup and delivery operations related to the assigned transport tasks, as well as to select the optimal path from the first stage’s output information to execute each operation simultaneously. Moreover, the experimental study validates the energy-saving effect of the established model and the effectiveness of the solution method, and the factors affecting the multi-load AGV EC are analyzed.