A receding horizon control-based holistic ant colony system approach for multi-runway aircraft arrival sequencing and scheduling

Aircraft arrival sequencing and scheduling (ASS) is a significant research problem that aims to relieve aircraft congestion in airports. Due to the increasing demand for air transportation and the limitation of runway capacity, effective and efficient scheduling approaches for handling the ASS problem are in great need for most modern airports. Ant colony system (ACS) in evolutionary computation is now commonly used to tackle the ASS problem due to its promising performance. However, most existing ACS-based algorithms are designed to tackle single-runway ASS problems or to tackle multi-runway ASS problems in a separative fashion (e.g., first considering the landing sequence and then considering the runway assignment, which will easily result in local optima). This paper the first time proposes a novel holistic ACS (HACS)-based scheduling approach for effectively solving the multi-runway ASS problem by scheduling the sequencing and the runways simultaneously. The proposed approach follows the local memetic feature of ASS that very late arrived aircraft are not likely to be scheduled to land very early, so as to divide the ASS problem into a set of subproblems using a receding horizon control technique and then to optimize each subproblem through the HACS algorithm. The advantage of HACS is that it can figure out the runway assignment of the aircraft in each receding horizon window as well as their landing sequence simultaneously in one stage, which is a global view to obtain the global optimal solution rather than the separative ACS algorithm that is easily trapped to local optima. Instances with different scales and different congestion modes are adopted to comprehensively evaluate the performance of the HACS approach. The experimental results show the superiority of HACS, especially in the large-scale, congested mode, and in scheduling environments with more runways.