Optimal Dynamic Airspace Configuration (DAC) based on State-Task Networks (STN)

The use of a more efficient allocation for meeting user needs is an essential element of the on-going transformation of the Air Traffic Management System (ATMS). Dynamic Airspace Configuration (DAC) presents several advantages with respect to traditional airspace management. DAC promises more flexible sector configurations capable to adapt to air traffic demand, complexity, and weather conditions. This is achieved by replacing static sector boundaries with a large number of airspace building blocks that could be merged depending upon the traffic conditions and resulting in a more dynamic airspace allocation. The use of flexible boundaries as a capacity management technique leads to more efficient flights and requires less site-specific training. In contrast, a flexible airspace allocation, as proposed by the DAC concept, allows more variables to be considered while continually adjusting the capacity to accommodate air traffic demand. The interchangeability of DAC building blocks (each airspace volume is merged with other(s)) is a large-scale optimization challenge. Hence, to obtain dynamic capacity, it is required to determine more efficient airspace allocation considering the distribution of air traffic demand and complexity among all building blocks. This paper presents a novel approach, in which the problem of merging and interchangeability of Dynamic Airspace Configuration is modelled using a single-layer State-Task Network (STN). The approach led to developing an optimization framework capable of efficiently allocating dynamic airspace volumes depending on the traffic demand and complexity. Subsequently, a use case containing 60 airspace building blocks using the DAC concept is defined over the Madrid ACC and solved using the developed framework and a Mixed Integer Programming (MIP) optimizer.