Sven Czarnian a1c48d7851 Merge branch 'feature/weather' into 'develop' | 3 years ago | |
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aman | 3 years ago | |
external | 3 years ago | |
icao | 3 years ago | |
src | 3 years ago | |
.gitignore | 3 years ago | |
.gitmodules | 3 years ago | |
README.md | 3 years ago | |
main.py | 3 years ago | |
setup.py | 3 years ago |
AMAN is splitted up into four different components.
AMAN uses Protocol Buffers for the message serialization and message definition between the EuroScope instance and the AMAN backend.
Additionally is ZeroMQ used for the communication abstraction layer.
This component provides the server backend with the planning and optimization system per airport. It is designed as a python framework that can run on a webserver. ZMQ based encryption and authentication methods are used to authenticate controllers.
Step 1: Initialization. Set up parameters for the RHC, and set the current receding horizon k = 1.
Step 2: Find out all the M aircraft whose PLTs belong to the kth receding horizon.
Step 3: Schedule the M aircraft in the kth receding horizon by using an ACS.
Step 4: Assign the aircraft whose ALTs belong to kth scheduled window ω(k) to land on the runway.
Step 5: Modify the PLT for those aircraft whose PLT belongs to ω(k) but the ALT does not belong to ω(k). The modification is to set their PLT to kTTI, making them belong to Ω(k + 1), such that they can be scheduled in the next receding horizon.
Step 6: Termination check. When all the aircraft have been assigned to land at the runway, the algorithm terminates. Otherwise, set k = k + 1 and go to Step 2 for the next receding horizon optimization.
In the preceding steps, Step 3 is the major process of the algorithm. The flowchart is illustrated on the right side of Fig. 3, and the details are given below.
Step 3.1: Schedule the M aircraft by the FCFS approach and calculate the fitness value through (3). Calculate the initial pheromone τ0 and set the pheromone for each aircraft pair as τ0.
Step 3.2: For each ant, do the following.
a) Determine the first landing aircraft s and construct the whole landing sequence using the state transition rule as (5) and (6).
b) Perform the local pheromone updating as (9).
Step 3.3: Calculate the fitness of each ant and determine the best solution. Moreover, the current best solution is compared with the historically best solution to determine the historically best solution.
Step 3.4: Perform the global pheromone updating as (10).
AMAN is released under the GNU General Public License v3