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System-Wide Optimization (SWO)

Domain: Gate-to-Gate
Principle Investigator: NASA Ames Research Center

The goal of System-Wide Optimization is to integrate various traffic management initiatives (e.g., Playbook re-routing, Ground Delay/Stop, Miles/Minutes-in-Trail restrictions) into a single cohesive plan that increases the capacity of the National Airspace system (NAS) by reducing delays and increasing traffic throughput. Other benefits include reduction in flow-manager workload, and more flexibility for aircraft operators to plan their flights.

This image shows a screenshot of the Future ATM Concept Evaluation Tool (FACET)

The research effort will proceed along two directions:

  • Structured Optimization integrates and improves Traffic Flow Management (TFM) actions in the current paradigm of NAS operations. TFM actions can be grouped into two categories. Re-routing actions are spatial controls that influence the paths of aircraft. Actions such as Miles/Minutes-in-Trail and Ground Delay/Stop are temporal controls that influence the position of an aircraft along its route. Structured Optimization seeks to improve throughput in a region of the NAS by using optimization methods to design an appropriate blend of spatial and temporal TFM actions.
  • Benchmark Optimization seeks the maximum capacity during ideal operations in a future NAS. It also serves as a reference point for evaluating approximations to ideal operations. Benchmark Optimization designs aircraft trajectories in a way that maximizes a NAS-wide (system level) objective function such as capacity or throughput. The technique for optimizing the trajectories of a large number (e.g., 15,000) of aircraft depends on several factors such as cost function, constraints, computational complexity, and uncertainty in both modeling and weather information.

Optimization methods such as dynamic programming, linear programming, graph theory, neural networks, simulated annealing, and genetic programming are suitable for system-wide optimization. Novel techniques for optimization based on near-optimal control will also be studied.

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