The taxi procedure at an airport refers to the surface movement of the aircraft between the parking position and the runway or vice versa. Nowadays, aircraft tend to taxi with the main engines, even if they are not optimized for it. Using an alternative suitable taxiing system that fulfills the requirements of the taxi procedure could be a useful tool to save costs. Automated guided vehicles (AGVs) seem to be a convenient alternative that could potentially be used for aircraft taxiing.

This report focuses on the state-of-the-art concept of using AGVs in the taxi procedure so that the vehicle tows the aircraft to the runway and the main engines of the aircraft are not used during the largest part of the taxi operations, reducing this way the cost of it. The main research objective is "to analyze the effect of using automated guided vehicles for aircraft taxiing at a major airport by creating a routing and scheduling model that is capable of creating trajectories for aircraft and automated guided vehicles while optimizing the cost of aircraft taxiing".


The use of AGVs started in 1955 in different situations and ever since its employment has continuously grown. With the current technologies an AGV system able to cope with the current throughput and reduce the cost of taxi operation could be developed. Indeed, it would be profitable for airlines keeping the throughput and airports.

After an exhaustive literature review of the topic, a routing and scheduling model that improves the current taxiing system has been created. This model takes into account the aircraft taxiing requirements and the optimal way of routing and scheduling with AGVs and is developed by Mixed Integer Linear Programming (MILP) in order to minimize the cost of the airport ground movement problem, including the cost of delay of the aircraft.


The model should be able to find the optimal solution for taxiing using AGVs in any major airport - in this research Amsterdam Airport Schiphol (AAS) has been used as case study. Historical flight data and the taxiing network of AAS are used to model the traffic on the taxi lanes. In this case study it was found that a small fleet of two narrow-body (NB) towing AGV and one wide-body (WB) towing AGV gives the highest cost savings for the analyzed days. The departures at AAS are not evenly distributed over the day, which affects the utilization rate of the vehicles. A roughly year estimation showed that 1.4 million EUR could be saved. Also 11 thousand tons of CO2 could be reduced, which means a plus of 82 thousand EUR to carbon offsetting cost savings in 2020.

By analyzing the effect of changing the input parameters a sensitivity analysis is made on the jet-fuel price, diesel price and the depreciation cost of the AGVs. While diesel price has a relative low effect on the cost savings using AGVs, these cost savings as well as the optimal fleet of vehicles is highly dependent on the jet-fuel price (the higher the jet fuel price is, the more cost savings can be obtained) and the depreciation cost of the vehicles. Since all these costs are an input to the model, it is possible to check whether it is cost-efficient to implement an AGV system based on the expected prices for an airport.

This research can be further improved by analyzing the effect of using AGVs on different airports and by testing more cases for AAS using up-to-date data. Another suggestion would be to decrease the computational time of the model to make it more user friendly to use for further research.