137 lines
6.3 KiB
Python
137 lines
6.3 KiB
Python
#!/usr/bin/env python
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from datetime import datetime, timedelta
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import numpy as np
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import random
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import sys
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import pytz
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from aman.sys.aco.Ant import Ant
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from aman.sys.aco.Configuration import Configuration
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from aman.sys.aco.RunwayManager import RunwayManager
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from aman.types.Inbound import Inbound
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# This class implements the ant colony of the following paper:
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# https://sci-hub.mksa.top/10.1109/cec.2019.8790135
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class Colony:
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def associateInbound(rwyManager : RunwayManager, inbound : Inbound, earliestArrivalTime : datetime, useITA : bool):
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rwy, eta, _ = rwyManager.selectArrivalRunway(inbound, useITA, earliestArrivalTime)
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eta = max(earliestArrivalTime, eta)
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inbound.PlannedRunway = rwy
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inbound.PlannedStar = inbound.ArrivalCandidates[rwy.Name].Star
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inbound.PlannedArrivalRoute = inbound.ArrivalCandidates[rwy.Name].ArrivalRoute
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inbound.PlannedArrivalTime = eta
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inbound.InitialArrivalTime = inbound.ArrivalCandidates[rwy.Name].InitialArrivalTime
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inbound.PlannedTrackmiles = inbound.ArrivalCandidates[rwy.Name].Trackmiles
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rwyManager.RunwayInbounds[rwy.Name] = inbound
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def calculateInitialCosts(rwyManager : RunwayManager, inbounds, earliestArrivalTime : datetime):
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overallDelay = timedelta(seconds = 0)
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# assume that the inbounds are sorted in FCFS order
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for inbound in inbounds:
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Colony.associateInbound(rwyManager, inbound, earliestArrivalTime, False)
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overallDelay += inbound.PlannedArrivalTime - inbound.InitialArrivalTime
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return overallDelay
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def __init__(self, configuration : Configuration):
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self.Configuration = configuration
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self.ResultDelay = None
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self.Result = None
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rwyManager = RunwayManager(self.Configuration)
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delay = Colony.calculateInitialCosts(rwyManager, self.Configuration.Inbounds, self.Configuration.EarliestArrivalTime)
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self.FcfsDelay = delay
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# run the optimization in every cycle to ensure optimal spacings based on TTG
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if 0.0 >= delay.total_seconds():
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delay = timedelta(seconds = 1.0)
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# initial value for the optimization
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self.Configuration.ThetaZero = 1.0 / (len(self.Configuration.Inbounds) * (delay.total_seconds() / 60.0))
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self.PheromoneMatrix = np.ones(( len(self.Configuration.Inbounds), len(self.Configuration.Inbounds) ), dtype=float) * self.Configuration.ThetaZero
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def optimize(self):
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# FCFS is the best solution
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if None != self.Result:
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return
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# define the tracking variables
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bestSequence = None
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# run the optimization loops
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for _ in range(0, self.Configuration.ExplorationRuns):
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# select the first inbound
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index = random.randint(1, len(self.Configuration.Inbounds)) - 1
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candidates = []
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for _ in range(0, self.Configuration.AntCount):
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# let the ant find a solution
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ant = Ant(self.PheromoneMatrix, self.Configuration)
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ant.findSolution(index)
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# fallback to check if findSolution was successful
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if None == ant.SequenceDelay or None == ant.Sequence or None == ant.SequenceScore:
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sys.stderr.write('Invalid ANT run detected!')
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sys.exit(-1)
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candidates.append(
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[
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ant.SequenceDelay,
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ant.Sequence,
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ant.SequenceScore,
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ant.SequenceDelay.total_seconds() / ant.SequenceScore
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]
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)
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# find the best solution in all candidates of this generation
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bestCandidate = None
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for candidate in candidates:
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if None == bestCandidate or candidate[3] < bestCandidate[3]:
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bestCandidate = candidate
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dTheta = 1.0 / ((candidate[0].total_seconds() / 60.0) or 1.0)
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for i in range(1, len(candidate[1])):
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update = (1.0 - self.Configuration.Epsilon) * self.PheromoneMatrix[candidate[1][i - 1], candidate[1][i]] + dTheta
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self.PheromoneMatrix[candidate[1][i - 1], candidate[1][i]] = max(update, self.Configuration.ThetaZero)
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# check if we find a new best candidate
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if None != bestCandidate:
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if None == bestSequence or bestCandidate[0] < bestSequence[0]:
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bestSequence = bestCandidate
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# create the final sequence
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if None != bestSequence:
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# create the resulting sequence
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self.ResultDelay = bestSequence[0]
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self.Result = []
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# finalize the sequence
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rwyManager = RunwayManager(self.Configuration)
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for i in range(0, len(bestSequence[1])):
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self.Result.append(self.Configuration.Inbounds[bestSequence[1][i]])
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Colony.associateInbound(rwyManager, self.Result[-1], self.Configuration.EarliestArrivalTime, True)
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# the idea behind the TTL/TTG per waypoint is that the TTL and TTG needs to be achieved in the
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# first 2/3 of the estimated trackmiles and assign it with a linear function to the waypoints
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# calculate the TTL/TTG for all the waypoints (TTG is positive)
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reqTimeDelta = abs((self.Result[-1].InitialArrivalTime - self.Result[-1].PlannedArrivalTime).total_seconds())
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gainTime = self.Result[-1].InitialArrivalTime >= self.Result[-1].PlannedArrivalTime
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m = -3 * reqTimeDelta / (2 * self.Result[-1].PlannedTrackmiles)
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timeDelta = 0.0
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for waypoint in self.Result[-1].PlannedArrivalRoute:
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waypointDT = m * waypoint.Trackmiles + reqTimeDelta
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timeDelta += waypointDT
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if timeDelta > reqTimeDelta:
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waypointDT -= timeDelta - reqTimeDelta
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waypointDT = timedelta(seconds = (waypointDT if False == gainTime else -1.0 * waypointDT))
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waypoint.PTA = waypoint.ETA + waypointDT
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# reached the PTA at the waypoint
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if timeDelta >= reqTimeDelta:
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break
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