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

Area of Science:

• Logistics and Supply Chain Management
• Operations Research
• Robotics and Automation

Background:

• Automated guided vehicles (AGVs) are crucial for intelligent logistics in manufacturing.
• Efficient path planning is essential for optimizing AGV operations and sustainability.
• Multi-load AGVs present unique challenges in task execution and energy management.

Purpose of the Study:

• To develop an energy-efficient path planning model for multi-load AGVs in manufacturing workshops.
• To optimize both transport distance and energy consumption (EC) as dual objectives.
• To enhance the sustainability of logistics processes through intelligent AGV routing.

Main Methods:

• A two-stage approach was proposed to solve the energy-efficient path planning problem.
• Stage 1: Acquiring optimal energy-efficient paths between nodes using a topological map of the workshop.
• Stage 2: Employing the non-dominated sorting genetic algorithm-II (NSGA-II) to determine optimal task sequences and path selections.

Main Results:

• The established model demonstrated a significant energy-saving effect for multi-load AGVs.
• The proposed two-stage solution method proved effective in optimizing AGV operations.
• Analysis identified key factors influencing multi-load AGV energy consumption.

Conclusions:

• The developed energy-efficient path planning model enhances the sustainability of AGV-based logistics.
• The NSGA-II based two-stage approach provides an effective solution for complex multi-load AGV routing problems.
• Understanding factors affecting AGV EC is vital for further optimization in intelligent manufacturing environments.