Optimization of automated guided vehicle scheduling model based on hybrid genetic algorithm and digital twin
Year: 2025
Type: article
Abstract: The scheduling of automated guided vehicles faces issues due to unpredictable data. To overcome this, research focuses on optimizing path planning and scheduling using hybrid genetic algorithms and digital twins. Firstly, a genetic algorithm improves the bat algorithm to avoid premature convergence to local optima, constructing a path planning model. Additionally, a scheduling optimization model based on digital twins is developed. This study used SolidWorks software to construct a 3D digital twin model of the warehouse environment, which synchronized the automated guided vehicles status in real-time through 10Hz sensor data and mapped it to the virtual model. Results show that the hybrid genetic algorithm achieves a shortest path of 31.88m and an average path of 32.31m, with a runtime of 26.33s (27.19s on average), outperforming the bat algorithm by 3.33s and the genetic algorithm by 2.86s. The dynamic scheduling method based on digital twins runs for 2614s with a 38.3% empty car rate. Furthermore, the proposed improved particle swarm optimization algorithm exhibits fast convergence, avoiding local optima. Overall, this model effectively enhances the loading and unloading efficiency in warehousing or port logistics, promoting industry development.
Source: Systems and Soft Computing
Authors: Li Liu, Yu Ding
Institution: Dalian Ocean University
Language: English
Subfield: Industrial and Manufacturing Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,870
OpenAlex ID: W4413289352