modeling swarm optimization on 4d arrivals
TRANSCRIPT
TransLabDepartament of Computer Science
University of Brasilia
Modeling the Swarm Optimization to Build EffectiveContinuous Descent Arrival Sequences
Vitor Filincowsky Ribeiro, Daniel Alberto Pamplona,Jose A. T. G. Fregnani, Italo Romani de Oliveira, Li Weigang
November 2nd 2016
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Introduction 4D Navigation Optimization theory AMAN 4D
Agenda
1 Introduction
2 4D Navigation
3 Optimization theory
4 AMAN 4D
VF Ribeiro, DA Pamplona, JTG Fregnani, IR Oliveira, W Li
Modeling the Swarm Optimization to Build Effective Continuous Descent Arrival Sequences
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Introduction 4D Navigation Optimization theory AMAN 4D
Introduction
VF Ribeiro, DA Pamplona, JTG Fregnani, IR Oliveira, W Li
Modeling the Swarm Optimization to Build Effective Continuous Descent Arrival Sequences
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Introduction 4D Navigation Optimization theory AMAN 4D
Introduction
Search for a shift in the Air Traffic Control and ManagementMethodology
Flight path of an aircraft through space and time (4D)
Performance Based Navigation procedures (PBN) to optimize andenhance the usage of air space resources
Descent, Climb and Cruise
Predicted an increase of 146% of the commercial fleet only in LatinAmerica
VF Ribeiro, DA Pamplona, JTG Fregnani, IR Oliveira, W Li
Modeling the Swarm Optimization to Build Effective Continuous Descent Arrival Sequences
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Introduction 4D Navigation Optimization theory AMAN 4D
Motivation
National ATM: establish the strategic evolution of theperformance-based National ATM System
Brazilian Air Force started an effort to implement PBN
Safety and performance needs of the National ATM program
Compliance to international requirements
VF Ribeiro, DA Pamplona, JTG Fregnani, IR Oliveira, W Li
Modeling the Swarm Optimization to Build Effective Continuous Descent Arrival Sequences
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Introduction 4D Navigation Optimization theory AMAN 4D
Motivation
Operations in Brazilian airports are performed without anycomputational support for decision making
Arrival sequencing is fully performed by the controller
Decision making process is error-prone
Impossible to evaluate the quality of results
VF Ribeiro, DA Pamplona, JTG Fregnani, IR Oliveira, W Li
Modeling the Swarm Optimization to Build Effective Continuous Descent Arrival Sequences
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Introduction 4D Navigation Optimization theory AMAN 4D
Motivation
Operations in Brazilian airports are performed without anycomputational support for decision making
Arrival sequencing is fully performed by the controller
Decision making process is error-prone
Impossible to evaluate the quality of results
VF Ribeiro, DA Pamplona, JTG Fregnani, IR Oliveira, W Li
Modeling the Swarm Optimization to Build Effective Continuous Descent Arrival Sequences
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Introduction 4D Navigation Optimization theory AMAN 4D
Objective
Main objective
Study and develop a computational methodology for the efficientmanagement of trajectories for commercial aircraft under 4D navigationparadigm
VF Ribeiro, DA Pamplona, JTG Fregnani, IR Oliveira, W Li
Modeling the Swarm Optimization to Build Effective Continuous Descent Arrival Sequences
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Introduction 4D Navigation Optimization theory AMAN 4D
4D Navigation
VF Ribeiro, DA Pamplona, JTG Fregnani, IR Oliveira, W Li
Modeling the Swarm Optimization to Build Effective Continuous Descent Arrival Sequences
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What is 4D Navigation
Trajectory
Four-dimensional (4D) flight path of an aircraft through space (3D) andtime (1D)
VF Ribeiro, DA Pamplona, JTG Fregnani, IR Oliveira, W Li
Modeling the Swarm Optimization to Build Effective Continuous Descent Arrival Sequences
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Trajectory Based Operations (TBO)
Aircraft’s navigation capability in space and time to improve efficiencyand predictability
Specified timing constraints at designated waypoints along the route
VF Ribeiro, DA Pamplona, JTG Fregnani, IR Oliveira, W Li
Modeling the Swarm Optimization to Build Effective Continuous Descent Arrival Sequences
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Flight Management System
Aircraft guidance along a pre-specified flight path
Arrival at the approach gate at a time specified by ATC
Flight planning capabilities for cost efficient operation
VF Ribeiro, DA Pamplona, JTG Fregnani, IR Oliveira, W Li
Modeling the Swarm Optimization to Build Effective Continuous Descent Arrival Sequences
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Continuous Descent Approach
CDA
Optimum descent profile while arriving at an airport
Continuously descending path, with a minimum of level flightsegments
Arriving aircraft descend from an optimal position with minimumthrust
VF Ribeiro, DA Pamplona, JTG Fregnani, IR Oliveira, W Li
Modeling the Swarm Optimization to Build Effective Continuous Descent Arrival Sequences
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Introduction 4D Navigation Optimization theory AMAN 4D
Continuous Descent Approach
VF Ribeiro, DA Pamplona, JTG Fregnani, IR Oliveira, W Li
Modeling the Swarm Optimization to Build Effective Continuous Descent Arrival Sequences
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Introduction 4D Navigation Optimization theory AMAN 4D
Goals of CDA
Enable the execution of a flight profile optimized to the operatingcapability of the aircraft
low engine thrust settingslow drag configuration
Reduce fuel burn and emissions during descent
Maximize operational efficiency while still addressing local airspacerequirements and constraints
VF Ribeiro, DA Pamplona, JTG Fregnani, IR Oliveira, W Li
Modeling the Swarm Optimization to Build Effective Continuous Descent Arrival Sequences
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Air Traffic Coordination
Roles of ATC
Landing sequence and traffic flow integration
En-route conflict detection and resolution
Insertion of vertical and horizontal separation
Concerns on safety and overall operation cost
VF Ribeiro, DA Pamplona, JTG Fregnani, IR Oliveira, W Li
Modeling the Swarm Optimization to Build Effective Continuous Descent Arrival Sequences
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Introduction 4D Navigation Optimization theory AMAN 4D
Air Traffic Coordination
Optimization task
Arrival coordination: aircraft want to arrive at a time within anoptimum time window
Aircraft sequencing at confluence points which collect incoming trafficfrom several airways
Individual costs as a matter of concern
Efficient operations
Arrival safety effectively ensured with a minimum, fair cost propagationamong the aircraft involved
VF Ribeiro, DA Pamplona, JTG Fregnani, IR Oliveira, W Li
Modeling the Swarm Optimization to Build Effective Continuous Descent Arrival Sequences
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Introduction 4D Navigation Optimization theory AMAN 4D
Air Traffic Coordination
Optimization task
Arrival coordination: aircraft want to arrive at a time within anoptimum time window
Aircraft sequencing at confluence points which collect incoming trafficfrom several airways
Individual costs as a matter of concern
Efficient operations
Arrival safety effectively ensured with a minimum, fair cost propagationamong the aircraft involved
VF Ribeiro, DA Pamplona, JTG Fregnani, IR Oliveira, W Li
Modeling the Swarm Optimization to Build Effective Continuous Descent Arrival Sequences
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Introduction 4D Navigation Optimization theory AMAN 4D
Optimization theory
VF Ribeiro, DA Pamplona, JTG Fregnani, IR Oliveira, W Li
Modeling the Swarm Optimization to Build Effective Continuous Descent Arrival Sequences
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Introduction 4D Navigation Optimization theory AMAN 4D
Swarm Optimization
Overall efficiency depends on the collective behavior of all agentsinvolved
Particle swarm: entities involved in social interactions can produceintelligence beyond the pure individual cognitive abilities
Particle Swarm Optimization (PSO)
All population members remain active until the end of processing
Iterations improve the quality of problem solutions over time
VF Ribeiro, DA Pamplona, JTG Fregnani, IR Oliveira, W Li
Modeling the Swarm Optimization to Build Effective Continuous Descent Arrival Sequences
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Introduction 4D Navigation Optimization theory AMAN 4D
Swarm Optimization
Overall efficiency depends on the collective behavior of all agentsinvolved
Particle swarm: entities involved in social interactions can produceintelligence beyond the pure individual cognitive abilities
Particle Swarm Optimization (PSO)
All population members remain active until the end of processing
Iterations improve the quality of problem solutions over time
VF Ribeiro, DA Pamplona, JTG Fregnani, IR Oliveira, W Li
Modeling the Swarm Optimization to Build Effective Continuous Descent Arrival Sequences
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Introduction 4D Navigation Optimization theory AMAN 4D
Particle Swarm Optimization (PSO)
Entities (particles) are placed in the search space
Objective function is evaluated at the current location
Next movement is determined by a combination of personal bestlocations with those of other members of the swarm
Eventually, particles move closer to an optimum of the fitness function
VF Ribeiro, DA Pamplona, JTG Fregnani, IR Oliveira, W Li
Modeling the Swarm Optimization to Build Effective Continuous Descent Arrival Sequences
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Introduction 4D Navigation Optimization theory AMAN 4D
Swarm Optimization
Typical PSO problem
a search space represented by a set of positions H = x
an application f defined on H constrained to a domainf : H → C = {c}a semiorder {c, c′} : c � c′ in C, meaning that c is better orequivalent to c′
a fitness function φ
VF Ribeiro, DA Pamplona, JTG Fregnani, IR Oliveira, W Li
Modeling the Swarm Optimization to Build Effective Continuous Descent Arrival Sequences
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Introduction 4D Navigation Optimization theory AMAN 4D
Swarm Optimization
For each particle, three D-dimensional vectors:
Current position
set of coordinates representing a point in the search space
Best position found so far
pbest (personal best) stores the solution value
Velocity
”heading”of the particle
~vi = ω1~vi + ω2~U(0, 1) · (~pi − ~xi) + ω3
~U(0, 1) · (~pg − ~xi)
~xi = ~xi + ~vi
VF Ribeiro, DA Pamplona, JTG Fregnani, IR Oliveira, W Li
Modeling the Swarm Optimization to Build Effective Continuous Descent Arrival Sequences
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Introduction 4D Navigation Optimization theory AMAN 4D
AMAN 4D
VF Ribeiro, DA Pamplona, JTG Fregnani, IR Oliveira, W Li
Modeling the Swarm Optimization to Build Effective Continuous Descent Arrival Sequences
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Introduction 4D Navigation Optimization theory AMAN 4D
Modeling 4D systems
FMS time window calculation
Conflict detection at merging points
Aircraft scheduling
AMAN 4D
VF Ribeiro, DA Pamplona, JTG Fregnani, IR Oliveira, W Li
Modeling the Swarm Optimization to Build Effective Continuous Descent Arrival Sequences
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Introduction 4D Navigation Optimization theory AMAN 4D
Modeling 4D systems
Merge point concept
All arriving aircraft should cross a specific waypoint in order toperform the descent until the approach fix
VF Ribeiro, DA Pamplona, JTG Fregnani, IR Oliveira, W Li
Modeling the Swarm Optimization to Build Effective Continuous Descent Arrival Sequences
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Introduction 4D Navigation Optimization theory AMAN 4D
Goals
Goals for Aircraft
Minimization of individual cost
Calculation of an optimal descent profile
Calculation of a feasible time window in order to reach the pointwhere CDA should take place
Goals for ATC
Conflict resolution for aircraft with overlapping TW at merging points
Calculate new values for cruise speed and altitude
Minimize total scenario cost by assigning updated TW
VF Ribeiro, DA Pamplona, JTG Fregnani, IR Oliveira, W Li
Modeling the Swarm Optimization to Build Effective Continuous Descent Arrival Sequences
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Introduction 4D Navigation Optimization theory AMAN 4D
Goals
Goals for Aircraft
Minimization of individual cost
Calculation of an optimal descent profile
Calculation of a feasible time window in order to reach the pointwhere CDA should take place
Goals for ATC
Conflict resolution for aircraft with overlapping TW at merging points
Calculate new values for cruise speed and altitude
Minimize total scenario cost by assigning updated TW
VF Ribeiro, DA Pamplona, JTG Fregnani, IR Oliveira, W Li
Modeling the Swarm Optimization to Build Effective Continuous Descent Arrival Sequences
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Introduction 4D Navigation Optimization theory AMAN 4D
Aircraft time window calculation
Cost Index evaluation
CI =Ctime
Cfuel
Profile 1: minimum flight time → delay control
Profile 2: minimum fuel burn → fuel cost control
VF Ribeiro, DA Pamplona, JTG Fregnani, IR Oliveira, W Li
Modeling the Swarm Optimization to Build Effective Continuous Descent Arrival Sequences
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Introduction 4D Navigation Optimization theory AMAN 4D
ATC decision making process
Ensure safety and minimize global costs
1 Receive TW as input from aircraft
2 Enqueue aircraft by arrival time
partial order
3 Check for overlapping time windows
conflict detection
4 Build search space from possible configurations that solves conflicts
5 Select the optimum state and notify aircraft
VF Ribeiro, DA Pamplona, JTG Fregnani, IR Oliveira, W Li
Modeling the Swarm Optimization to Build Effective Continuous Descent Arrival Sequences
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Introduction 4D Navigation Optimization theory AMAN 4D
ATC decision making process
Particle Swarm decision making
Each conflicting aircraft will be a variable to compose the particles
Random time windows will be applied to each aircraft
randomization is constrained to the minimum and maximumperformance values
The fitness function φ is the overall cost to the scenario
estimate by ATC that says how good is the position occupied by theparticle
φj = −n∑
i=1
kji
VF Ribeiro, DA Pamplona, JTG Fregnani, IR Oliveira, W Li
Modeling the Swarm Optimization to Build Effective Continuous Descent Arrival Sequences
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Introduction 4D Navigation Optimization theory AMAN 4D
ATC decision making process
Particle Swarm decision making
VF Ribeiro, DA Pamplona, JTG Fregnani, IR Oliveira, W Li
Modeling the Swarm Optimization to Build Effective Continuous Descent Arrival Sequences
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Aircraft implementation
Aircraft in the prototype act as agents that have their ownperformance standards
Simulated datalink communication between ATC and aircraft
VF Ribeiro, DA Pamplona, JTG Fregnani, IR Oliveira, W Li
Modeling the Swarm Optimization to Build Effective Continuous Descent Arrival Sequences
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ATC implementation
Arrival schedules and speeding instructions
Receive TW and schedule aircraft
Cost Index and specific performance parameters are unknown by theATC, but the time parameters are well-known
Particle Swarm optimization algorithm
VF Ribeiro, DA Pamplona, JTG Fregnani, IR Oliveira, W Li
Modeling the Swarm Optimization to Build Effective Continuous Descent Arrival Sequences
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ATC - Particle Swarm optimizer
Setup of default parameters
Each dimension in the particle represents an aircraft
Random positions and random velocities are attributed to eachparticle
positions represent arrival scheduleseach variable in the position vector is a time constraint for an aircraft
The fitness function is the shared global objective function to beminimized
VF Ribeiro, DA Pamplona, JTG Fregnani, IR Oliveira, W Li
Modeling the Swarm Optimization to Build Effective Continuous Descent Arrival Sequences
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Introduction 4D Navigation Optimization theory AMAN 4D
ATC - Particle Swarm optimizer
Each particle will occupy a different position in its immediateneighborhood after each algorithmic iteration
Updating the velocity of a particle changes its motion profile towardbetter positions
The best particle in the swarm is the one that assigns the less costlytime slots for the whole aircraft set
At the end of the processing, the best position found so far is the finalsolution
VF Ribeiro, DA Pamplona, JTG Fregnani, IR Oliveira, W Li
Modeling the Swarm Optimization to Build Effective Continuous Descent Arrival Sequences
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Simulations and results
Test scenario: Presidente Juscelino Kubitschek International Airport inBrasılia (SBBR)
(2015) Domestic International Total
Aircraft 180972 5405 186377
PAX 19110040 711756 19821796
Cargo (kg) 37939488 1528177 39467665
No actual implementation of MP
a virtual MP is created by 150 nautical miles at landing runway
All arriving flights are selected at SBBR from 11:00am to 11:30amduring a regular workday
total of 26 flights scheduled
VF Ribeiro, DA Pamplona, JTG Fregnani, IR Oliveira, W Li
Modeling the Swarm Optimization to Build Effective Continuous Descent Arrival Sequences
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Simulations and results
Objective function is set to minimize the delay costs of each aircraftindividually
Fitness function corresponds to the estimated delay cost of the targettime assigned to aircraft
Every target time is checked in order to detect conflicts and off-limitspeed performances
When the simulation is ended, the best particle found is collected andthe corresponding target times are extracted
Result: 77% of the flights are able to have their desired TWaccomplished
VF Ribeiro, DA Pamplona, JTG Fregnani, IR Oliveira, W Li
Modeling the Swarm Optimization to Build Effective Continuous Descent Arrival Sequences
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Simulations and results
VF Ribeiro, DA Pamplona, JTG Fregnani, IR Oliveira, W Li
Modeling the Swarm Optimization to Build Effective Continuous Descent Arrival Sequences
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Conclusions
An AMAN system counting on two solutions of CDA has beendeveloped as a POC
Arrival coordination with PSO algorithm considers air traffic safetyand individual requirements in the extent of possibilities
Flights issued to later periods are more receptive to absorb delays
Coordination with other air traffic services is needed for proper flowcontrol
Individual interests of aircraft were successfully combined withairspace control constraints
VF Ribeiro, DA Pamplona, JTG Fregnani, IR Oliveira, W Li
Modeling the Swarm Optimization to Build Effective Continuous Descent Arrival Sequences
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Thank you!
VF Ribeiro, DA Pamplona, JTG Fregnani, IR Oliveira, W Li
Modeling the Swarm Optimization to Build Effective Continuous Descent Arrival Sequences