Multi-AGVs path planning based on improved ant colony algorithm

The scheduling problem in multi-automated guided vehicles (AGVs) system involves the job-shop scheduling problem and the vehicle routing problem. In the real world, the scheduling problem is limited by some constraint conditions such as the system should be able to avoid collisions and route correction is asked to be easily realized. This paper studies the scheduling and collision-free routing problem of AGVs. Mathematical programming model is given for this problem, and the algorithm is improved based on multi-objective programming to optimize the pheromone matrix. By calculation using available test problems, the performance of the two methods is compared. The improved ant colony algorithm is empirically evaluated. The result shows that the mathematical programming model has good effect but limited application scope. The improved algorithm improves the performance of the existing algorithm, and finally, the rationality of the improved algorithm for large instance key parameter settings and scheme selection is verified by eleven test samples.