Cognitive dynamic airspace management
This thesis focuses on optimisation methods in airspace management. A given airspace is typically divided into sectors where a pair of air traffic controllers are assigned to ensure safety in each sector. The controllers’ workload can be generally divided into interactions with (a) pilots in the...
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Format: | Theses and Dissertations |
Language: | English |
Published: |
2019
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Online Access: | https://hdl.handle.net/10356/103942 http://hdl.handle.net/10220/47796 |
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Institution: | Nanyang Technological University |
Language: | English |
Summary: | This thesis focuses on optimisation methods in airspace management. A given airspace is
typically divided into sectors where a pair of air traffic controllers are assigned to ensure
safety in each sector. The controllers’ workload can be generally divided into interactions
with (a) pilots in the given sector and (b) other controllers when the aircraft exits their
sector and enters into another sector. These interactions are referred to the monitoring
and coordination workloads respectively. This research work specifically studies the
optimisation methods applied to the optimal design of sector shapes in four aspects.
• Static state: The formulation of finding sector shapes have been mostly proposed in
a single objective optimisation framework. However, given the conflicting nature of
the considerations, the problem should be formulated as a multi-objective problem
incorporating the preferences of the user.
• Dynamic state: Re-sectorization strategies for reconfiguration or change in sector
shapes have been suggested to handle the changing air traffic flow. However, the
dynamic airspace sectorization problem (DAS) has not been explored as a system
that has varying traffic flows over time.
• Weather: Weather plays a significant role in the availability of airspace. However,
most work that addresses changing weather conditions focuses on the re-routing of
affected airways, which could, in turn, cause an imbalance of controllers workload.
The combination of re-routing and resectorization should be considered simultaneously
under the effect of limited available airspace.
• Multi-objective solver: Most multi-objective solvers stem from evolutionary algorithms
(EAs) due to its population-based nature. Multi-objective EAs are fundamentally stochastic in nature, hence solutions are likely to be irreproducible. For reproducible and reliable solutions, deterministic algorithms are preferable.
In this thesis, a preference-based bi-objective optimisation model that optimises sector
shapes for a given set of traffic flows and airspace is first presented. The two objectives
are i) minimizing the standard deviation of the monitoring workload (within the sector)
among pairs of controllers and ii) minimizing the total coordination workload between
sectors. The proposed model aims to obtain traffic flow conforming sectors while equally
distributing the monitoring workload among controllers as much as possible. Furthermore,
preference-based methods were used to help the solver focus on the particular region
of interest on the approximate Pareto front. The proposed preference-based strategy was
found to obtain a wider range of feasible solutions when compared to a constraint-based
strategy.
Given dynamically changing traffic flows, a single set of sector shapes could not remain
optimal over time. Hence, the airspace management problem should be deemed
as a system over time, re-sectorizing sectors when needed. The cognitive decision making
architecture for dynamic airspace sectorization (CDAS) is first proposed to answer
the questions on when-to-do and how-to-do a resectorization. With a multi-objective
framework, CDAS provides the decision maker with the predicted performance objectives
(based on flight plans) of available optimal sector shapes (for selection) for the next
time period. This could allow the decision maker to avoid a need for resectorization in
the next time period. However, there are still some uncertainties present in the feasibility
of the sector shapes in future time intervals. Focusing on the benefits of planning, the
airspace management problem is fitted into a rolling horizon optimisation framework with
a single objective optimisation model. This approach optimizes the sector shapes with
the consideration of traffic flows in the next few time intervals. In comparison with the
single time interval optimisation, the proposed method is able to provide better feasible
solutions over a time horizon.
On top of varying traffic flows, dynamic weather conditions can affect the availability
of airspace. The Simultaneous Optimisation of the AirWay and AirSpace (SAWAS) is
proposed to address this changing availability of airspace. The model seeks to balance the ATC monitoring workload, minimize the total coordination workload and maximize
the similarity of sector shapes with the initial sectorization. An experimental study was
performed to compare this methodology with a sequential design approach. It was found
that the concurrent consideration of sector re-design and flow re-routing on the design
objectives yields better and more optimal solutions.
The reproducibility and reliability of solutions in real-world problems are not guaranteed
with stochastic solvers. Therefore, a deterministic indicator-based multi-objective
Multi-scale Search Optimisation (MSO) algorithm, Pareto-Aware Dividing Rectangles
(PA-DIRECT) is proposed to tackle this issue. PA-DIRECT is benchmarked against
non-dominance-based multi-objective MSO algorithm, MO-DIRECT and popular evolutionary
algorithms on a bi-objective test set on the Comparing Continuous Optimisers
(COCO) platform. The study results affirm the performance of PA-DIRECT in providing
a high-quality approximate set, particularly for multi-modal problems. Further,
PA-DIRECT is used to solve the aforementioned preference-based bi-objective optimisation
model with general constraint handling techniques.
In summary, the thesis first introduces a preference-based approach for the designing
of optimal sector shapes. Next, two different frameworks that consider future traffic flows
are proposed for DAS. Following that, the changing availability of airspace is tackled by
the simultaneous optimisation of re-routing and resectorization. Last but not least, a
deterministic multi-objective solver is developed to find optimal airspace sector shapes.
Future works include: i). a reference-point-based MSO algorithm for optimizing sector
shapes; ii). interactive multi-objective optimisation approaches for optimizing sector
shapes; iii). extending the rolling horizon optimisation framework with a multi-objective
optimisation model; iv). solving the rolling horizon optimisation framework with a reinforcement
learning approach; v). implementing SAWAS as a module in the CDAS
framework; and vi). simulations for a better measurement of controllers’ workload. |
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