Managing Air Traffic Flow With Link Flow Rate Control: A Pure Integer Programming Model With More Accurate Link Connection Modeling

Abstract

This paper develops a mathematical optimization approach for link-level air traffic flow management. First, a pure integer programming model is established for the problem on a link level. The model seeks to minimize the weighted summation of ground delay and airborne delay, and offers a more precise modeling for the capacity and traffic dynamics at the merge, diverge, and intersection nodes. The optimization problem is computationally NP-hard, and a modified genetic algorithm is then presented to solve it. Besides a new chromosome coding rule for converting binary decision into time series vectors, the algorithm uses a taboo mutation operator and an adaptive mutation probability selection operator. Finally, the effectiveness and superiority of the model and algorithm are demonstrated through a real-world case study in Dalian, China, and some managerial insights are presented.