A multi-modal optimization model improves energy consumption and response time in automated guided vehicles, indicating better efficiency in smart manufacturing.
Key Points
Energy consumption for automated guided vehicles was reduced by 21.3%, enhancing efficiency significantly.
The dynamic task allocation strategy combines ant colony optimization and entropy-based methods, improving resource use.
A simulation compared results with a genetic algorithm, revealing superior performance in path optimization.
The model offers theoretical support for design in intelligent manufacturing, minimizing efficiency losses in high-density scenarios.