Research on scheduling algorithm of multi-AGVS system

Due to the rapid development of global economy, the demand for Automated Guided Vehicles System(AGVS) scheduling is increasing in the production and transportation industry. As an automatic guide device that can transport a specified item to a specified location, Automated Guided Vehicles (AGV) greatly reduces the transportation efficiency and transportation cost. Among the basic AGVS scheduling, task management and path control are the two most widely studied and valued tasks. We construct a new integration model for scheduling problem and conflict-free routing problem of multiple AGVs. A new mathematical programming model is developed, and the ant colony algorithm (ACA) is applied to solve the model. The algorithm is optimized based on multi-objective programming, working similarity and pheromone matrix. A scheduling experiment is used to find the optimal path, and the performance of ACA is compared with that of precise algorithm. The results show that although the precise algorithm can accurately solve small scale problems, the improved ant colony algorithm is more suitable for medium and large scale problems than the precise algorithm, which shows the credibility of ACA to solve medium and large scale problems. Therefore, it can be concluded that the improved ant colony algorithm has the ability to provide task management and path planning for large-scale AGVS scheduling problems in an acceptable time range.