Some general considerations.

README.md

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 # aman-sys
 
+Dieses Repository stellt den VATGER AMAN Server bereit, dessen Aufgabe es ist, einen optimalen Arrivalflow für die Flugplätze in der VACC zu errechnen.
+
+
+## RHC-ACS-ASS Algorithm
+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).

algorithm/rhcacsass.py

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+# RHC-ACS-ASS Algorithm
+# This class is written to contain an implementation
+# of the Ant Colony System based upon the Receding Horizon
+# for Aircraft Arrival Sequencing
+
+class RhcAcsAss:
+    k = 1  # Receding horizon counter
+    N_rhc = 4  # The number of receding horizons
+    T_ti = 1  # The scheduling window
+
+    def __init__(self, n, t):
+        self.N_rhc = n
+        self.T_ti = t
+
+    def find_aircraft_for_horizon(self, ki):
+        # Omega(k) = [(k-1) * T_ti, (k+N_rhc-1) * T_ti]
+        pass
+    

icao/recat.py

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+# Recat departure separation in seconds
+# x = CAT A -> CAT F
+# y = CAT A -> CAT F
+# https://www.skybrary.aero/index.php/RECAT_-_Wake_Turbulence_Re-categorisation
+recatDeparture = [
+    [0, 100, 120, 140, 160, 180],
+    [0, 0, 0, 100, 120, 140],
+    [0, 0, 0, 80, 100, 120],
+    [0, 0, 0, 0, 0, 120],
+    [0, 0, 0, 0, 0, 100],
+    [0, 0, 0, 0, 0, 80],
+]
+
+#Recat Arrival in NM
+recatArrival = [
+    [3, 4, 5, 5, 6, 8],
+    [0, 3, 4, 4, 5, 7],
+    [0, 0, 3, 3, 4, 6],
+    [0, 0, 0, 0, 0, 5],
+    [0, 0, 0, 0, 0, 4],
+    [0, 0, 0, 0, 0, 3],
+]