A Fleet Management Algorithm for Automatic Taxi Operations

The continuous growth of air traffic has resulted in congestion and long queues at airports, causing delays, pollution and loss of money for the airlines. In this paper, we present a new solution to perform just in time taxi operations using autonomous electric towbarless tractors. The purpose of this solution is to eliminate queues and to reduce the environmental and economic impact of ground operations, meeting the requirements for the future air traffic management (ATM). An algorithm for a tool that provides conflict-free schedules for the tractor autopilots will be presented; the generated schedule is meant to minimize the overall cost of the ground operations. The proposed algorithm is based on a hybrid particle swarm optimization (HPSO), hybridized with a hill-climb meta-heuristic, that defines an optimal set of path and speed for each flight in the flight schedule. The effectiveness of the proposed HPSO algorithm compared to the classical particle swarm optimization (PSO) was studied. Results showed that the proposed algorithm is more effective with respect to the classical PSO, even though the required computational time drastically increases.