FACET (F uture A ir Traffic Management C oncepts E valuation T ool) is a simulation and analysis tool being developed at the NASA Ames Research Center. This paper introduces the design, architecture, functionalities and applications of FACET. The purpose of FACET is to provide a simulation environment for exploration, development and evaluation of advanced Air Traffic Management concepts. Examples of these concepts include new Air Traffic Management paradigms such as Distributed Air/Ground Traffic Management, advanced Traffic Flow Management, and new Decision Support Tools for controllers working within the operational procedures of the existing air traffic control system. FACET models system-wide en route airspace operations over the contiguous United States. The architecture of FACET strikes an appropriate balance between flexibility and fidelity. This feature enables FACET to model airspace operations at the U.S. national level, and process over 5,000 aircraft on a single desktop computer running on any of a wide variety of operating systems. FACET has been designed with a modular software architecture to facilitate rapid prototyping of diverse Air Traffic Management concepts. FACET has prototypes of several advanced Air Traffic Management concepts: airborne self-separation; a Decision Support Tool for direct routing; advanced Traffic Flow Management techniques utilizing dynamic density predictions for airspace redesign and aircraft rerouting; and, the integration of space launch vehicle operations into the U.S. National Airspace System.
We present the mapping of a class of simplified air traffic management (ATM) problems (strategic conflict resolution) to quadratic unconstrained boolean optimization (QUBO) problems. The mapping is performed through an original representation of the conflictresolution problem in terms of a conflict graph, where nodes of the graph represent flights and edges represent a potential conflict between flights. The representation allows a natural decomposition of a real world instance related to wind-optimal trajectories over the Atlantic ocean into smaller subproblems, that can be discretized and are amenable to be programmed in quantum annealers. In the study, we tested the new programming techniques and we benchmark the hardness of the instances using both classical solvers and the D-Wave 2X and D-Wave 2000Q quantum chip. The preliminary results show that for reasonable modeling choices the most challenging subproblems which are programmable in the current devices are solved to optimality with 99% of probability within a second of annealing time.
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