Integrated Task Assignment and Path Planning for Electric Towing Vehicle Operations at a Major Hub Airport

Abstract

Airport surface movement operations present a promising opportunity for enhancing airport sustainability, as up to 25% of airport emissions originate from taxiing aircraft. This study explores the use of Electric Towing Vehicles (ETVs) to tow aircraft to runways, eliminating the operational use of aircraft engines during taxiing. Given the high cost of ETVs, their effective deployment is essential. This research adopts a Multi-Agent Pickup and Delivery (MAPD) framework to optimize ETV operations, addressing both the task assignment and path planning aspects of this problem. A novel task assignment method based on Ant Colony Optimization (ACO) is proposed, capable of efficiently scheduling towing and charging tasks for ETVs. The proposed method outperforms benchmark Mixed Integer Linear Programming (MILP) methods in terms of runtime and scalability, while delivering a competitive solution quality. The ACO-based task assignment method is integrated with a state-of-the-art motion planning algorithm based on Priority Based Search (PBS) and Safe Interval Motion Planning (SIMP). The resulting MAPD solution method is evaluated through its application in a high-fidelity simulation of Amsterdam Airport Schiphol (AAS). The simulation results indicate that deploying 22 ETVs at AAS can achieve a 57% reduction in the annual addressable CO2 emissions, while ensuring the economic feasibility of the operational concept.