Airport AGV path optimization model based on ant colony algorithm to optimize Dijkstra algorithm in urban systems

Abstract

Nowadays, with the rapid development of intelligent logistics and Automated Guided Vehicles (AGV), the application scenario areas of AGVs are becoming more and more widely. This research takes the application of AGV in airport baggage loading scenarios as the research direction. Most of the airport AGV path planning does not capitalize on the time window of the early arrival of baggage, which leads to the prolonged running time of AGVs. To reduce the operating cost and path cost of AGV, this research proposed an airport AGV path optimization model based on the Dijkstra algorithm of ant colony optimization (ACO-DA). The model considered the environment with obstacles, first of all, the baggage pickup sequencing by ant colony optimization, and in the second place, the AGV path planning by integrating with Dijkstra’s algorithm. Last, the model was simulated and analyzed, and its proposed fusion algorithm performed better than the path planning models of the other three algorithms in airport baggage check-in, and its paths were respectively shortened by 2.3%, 2.64%, and 6.06%.