A n − D ant colony optimization with fuzzy logic for air traffic flow management

Recent studies show that the number of flights is expected to be increased significantly by 2030, leading to air traffic capacity and congestion issues in the air sectors. This challenging management of the anticipated volume of flights has emerged new derivatives and procedures from the European Union and EUROCONTROL. Aligned with the new vision of future Air Traffic Flow Management (ATFM), such as Trajectory Based Operations, this study proposes a mixed integer nonlinear formulation of ATFM based on 4D trajectories and free flight aspects. The model targets to minimize the total costs derived from airborne and ground holding delays, speed deviations, route alterations and cancellation policies. To solve the proposed nonlinear formulation, a novel n − D ant colony optimization algorithm integrated with fuzzy logic (n − DACOF) is presented. Each flight level is represented as graph and the n − D stands for the n number of permitted flight levels. n − DACOF can solve the ATFM problem by constructing a route moving among n graphs. Due to the multi-objective formulation, fuzzy logic permits the qualitative evaluation of the generated routes by the algorithm. The results showed that n − DACOF outperformed the baseline algorithm ACO, as well as, the CPLEX solver within computing time limits.