Research on Multi-AGV Task Allocation in Train Unit Maintenance Workshop

In the context of the continuous development and maturity of intelligent manufacturing and intelligent logistics, it has been observed that the majority of vehicle maintenance in EMU trains still relies on traditional methods, which are characterized by excessive manual intervention and low efficiency. To address these deficiencies, the present study proposes the integration of Automatic Guided Vehicles (AGVs) to improve the traditional maintenance processes, thereby enhancing the efficiency and quality of vehicle maintenance. Specifically, this research focuses on the scenario of the maintenance workshop in EMU trains and investigates the task allocation problem for multiple AGVs. Taking into consideration factors such as the maximum load capacity of AGVs, remaining battery power, and task execution time, a mathematical model is formulated with the objective of minimizing the total distance and time required to complete all tasks. A multi-population genetic algorithm is designed to solve the model. The effectiveness of the proposed model and algorithm is validated through simulation experiments, considering both small-scale and large-scale scenarios. The results indicate that the multi-population genetic algorithm outperforms the particle swarm algorithm and the genetic algorithm in terms of stability, optimization performance, and convergence. This research provides scientific guidance and practical insights for enterprises adopting task allocation strategies using multiple AGVs.