Optimal Integration of Departures and Arrivals in Terminal Airspace

Abstract

Coordination of operations with spatially and temporally shared resources, such as route segments, fixes, and runways, improves the efficiency of terminal airspace management. Problems in this category are, in general, computationally difficult compared with conventional scheduling problems. This paper presents a fast time algorithm formulation using a nondominated sorting genetic algorithm. It was first applied to a test problem introduced in existing literature. An experiment with a test problem showed that new methods can solve the 20 aircraft problem in fast time with a 65% or 440 s delay reduction using shared departure fixes. To test its application in a more realistic and complicated problem, the nondominated sorting genetic algorithm was applied to a problem in Los Angeles terminal airspace, where interactions between 28% of Los Angeles arrivals and 10% of Los Angeles departures are resolved by spatial separation in current operations, which may introduce unnecessary delays. In this work, three types of separations (spatial, temporal, and hybrid separations) were formulated using the new algorithm. The hybrid separation combines both temporal and spatial separations. Results showed that, although temporal separation achieved less delay than spatial separation with a small uncertainty buffer, spatial separation outperformed temporal separation when the uncertainty buffer was increased. Hybrid separation introduced much less delay than both spatial and temporal approaches. For a total of 15 interacting departures and arrivals, when compared with spatial separation, the delay reduction of hybrid separation varied between 16% or 4.4 min and 73% or 12.1 min, corresponding to an uncertainty buffer from 0 to 60 s. Furthermore, as a comparison with the nondominated sorting genetic algorithm, a first-come/first-serve-based heuristic method was implemented for the hybrid separation. Experiments showed that the results from the nondominated sorting genetic algorithm have 14–55% less delay than the heuristic method with varied uncertainty buffer sizes.