rolling horizon approach for aircraft scheduling in the terminal control area of busy airports
DESCRIPTION
Rolling Horizon Approach For Aircraft Scheduling In The Terminal Control Area Of Busy Airports Andrea D’Ariano , ROMA TRE University. ISTTT, 03/10/2014. 1. Presentation outline. Introduction Modeling a Terminal Control Area Solution Framework and Algorithms Computational Experiments - PowerPoint PPT PresentationTRANSCRIPT
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Dipartimento di Ingegneria
Rolling Horizon Approach For Aircraft Scheduling In The Terminal Control Area Of Busy Airports
Andrea D’Ariano, ROMA TRE University1ISTTT, 21/04/23
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Junior ConsultingDipartimento di Ingegneria
IntroductionModeling a Terminal Control Area Solution Framework and AlgorithmsComputational ExperimentsConclusions and Ongoing Research
Presentation outlinePresentation outline
This work was partially supported by the Italian Ministry of Research, project FIRB “Advanced tracking system in intermodal freight transportation”.
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Junior ConsultingDipartimento di Ingegneria
Air Traffic Control (ATC)Air Traffic Control (ATC)
Air traffic control must ensure safe, ordered and rapid transit of aircraft on the ground and in the air segments.
[*] Source: EUROCONTROL Short-term forecast 2009
With the increase in air traffic [*], aviation authorities are seeking methods (i) to better use the existing airport infrastructure, and (ii) to better manage aircraftmovements in the vicinity of airports during operations.
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Status of the current ATC practiseStatus of the current ATC practise• Airports are becoming a major bottleneck in ATC operations. • The optimization of take-off/landing operations is a key factor to improve the performance of the entire ATC system.
• ATC operations are still mainly performed by human controllers whose computer support is most often limited to a graphical representation of the current aircraft position and speed. • Intelligent decision support is under investigation in order to reduce the controller workload (see e.g. recent ATM Seminars).
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Detailed(e.g. Bianco, Dell’Olmo, Giordani)
Basic(e.g. Bertsimas, Lulli, Odoni )
Literature: Aircraft Scheduling Problem (ASP)Literature: Aircraft Scheduling Problem (ASP)
Existing Approaches Dynamic
(e.g. Beasley, Ernst)
Static(e.g. Dear, Hu, Chen)
Chris Potts
et al. 2
011
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Junior ConsultingDipartimento di Ingegneria
Literature: Research needs & directionsLiterature: Research needs & directions
Aircraft Scheduling Problem (ASP) in Terminal Control Areas:
Most aircraft scheduling models in literature represent the TCA as a single resource, typically the runway. These models are not realistic since the other TCA resources are ignored.
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We present the “alternative graph” approach for the accurate modelling of air traffic flows at multiple runways and airways.
This approach has already been applied successully to control railway traffic for metro lines and railway networks.
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Junior ConsultingDipartimento di Ingegneria
Our approach for TCAsOur approach for TCAs
Design, implementation and testing of:• a dynamic (rolling horizon) setting• a detailed (alternative graph) modeling• heuristic and exact (branch & bound) ASP algorithms
Research questions:1.how does the traffic control system react when disturbances arise,2.when and how is it more convenient to reschedule aircraft in the TCA,3.which algorithm performs best in terms of delay and travel time minimization,4.which algorithm is the less sensitive to disturbances.
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Junior ConsultingDipartimento di Ingegneria
IntroductionModeling a Terminal Control Area Solution Framework and AlgorithmsComputational ExperimentsConclusions and Ongoing Research
Presentation outlinePresentation outline
This work was partially supported by the Italian Ministry of Research, project FIRB “Advanced tracking system in intermodal freight transportation”.
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Junior ConsultingDipartimento di Ingegneria
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MXP TCA :(MILAN, ITALY)
FCO TCA :(ROME, ITALY)
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• The quality of a schedule is measured in terms of maximum delay minimization (limiting the delay caused by potential conflicts).
• Fixed constraints in F model feasible timing for each aircraft on its specific route, plus constraints on each resource of its route.
• Alternative constraints in A represent the aircraft ordering decision at air segments and runways, plus decisions on holding circles.
• A feasible schedule is an event graph with no positive length cycles.
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The Alternative Graph (AG) ModelThe Alternative Graph (AG) ModelMascis & Pacciarelli 2002
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AG Model AG Model
A1
0 *
αA
Release date αA
(αA = expected entry time of aircraft A)
Air Segments
CommonGlide Path RunwaysHolding Circles
8
16
173
SRN
1
TOR
MBR
2 6
4 10
11
12
15
7
513
14
RWY 35L
RWY 35R
9
A
11
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Junior ConsultingDipartimento di Ingegneria
AG Model AG Model
Entry due date βA ( βA = - αA )
Air Segments
CommonGlide Path
RunwaysHolding Circles
8
16
173
SRN
1
TOR
MBR
2 6
4 10
11
12
15
7
513
14
RWY 35L
RWY 35R
9
A
A1
0 *
αA
βA
12
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(A4, A1)No holding circle (holding time = 0)
(A1, A4)Yes holding circle (holding time = δ)
AG Model AG Model Air Segments
CommonGlide Path
RunwaysHolding Circles
8
16
173
SRN
1
TOR
MBR
2 6
4 10
11
12
15
7
513
14
RWY 35L
RWY 35R
9
A
A1 A4
0 *
αA
βA
13
δ
0
-δ
0
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AG Model AG Model
A1 A4 A10
0 *
αA
βA
Time window for the travel time in each air segment[min travel time; max travel time]
Air Segments
CommonGlide Path Runways
Holding Circles
8
16
173
SRN
1
TOR
MBR
2 6
4 10
11
12
15
7
513
14
RWY 35L
RWY 35R
9
A
14
min
- max
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Junior ConsultingDipartimento di Ingegneria
CommonGlide Path Runways
Holding Circles Air Segments
8
16
173
SRN
1
TOR
MBR
2 6
4 10
11
12
15
7
513
14
RWY 35L
RWY 35R
9
AAAG Model AG Model
A1 A4 A15A10 A13 AOUTA16
0 *
αA
βA
γA
Exit due date γA
(γA = - planned landing time)
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Junior ConsultingDipartimento di Ingegneria
CommonGlide Path Runways
Holding Circles Air Segments
8
16
173
SRN
1
TOR
MBR
2 6
4 10
11
12
15
7
513
14
RWY 35L
RWY 35R
9
AA
BB
AG Model AG Model
A1 A4 A15A10 A13 AOUTA16
0 *
B3 B8 B15B12 B14 BOUTB17
αA
αB
βA
γA
βB
γB
Aircraft ordering problem between A and B on the common glide path (resource 15) : Longitudinal and diagonal distances have to be respected
Potential conflicton resource 15 !
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Junior ConsultingDipartimento di Ingegneria
CommonGlide Path Runways
Holding Circles Air Segments
8
16
173
SRN
1
TOR
MBR
2 6
4 10
11
12
15
7
513
14
RWY 35L
RWY 35R
9
AA
BB
C
C
AG Model AG Model
A1 A4 A15A10 A13 AOUTA16
0 *
B3 B8 B15B12 B14 BOUTB17
αA
αB
βA
γA
βB
γB
COUTC17
γC
αC
Aircraft ordering problem between B and C for the runway (resource 17): This is a no-store resource!
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Junior ConsultingDipartimento di Ingegneria
IntroductionModeling a Terminal Control Area Solution Framework and AlgorithmsComputational ExperimentsConclusions and Ongoing Research
Presentation outlinePresentation outline
This work was partially supported by the Italian Ministry of Research, project FIRB “Advanced tracking system in intermodal freight transportation”.
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Developing a decision support toolDeveloping a decision support toolFrom a logical point of view, ATC decisions can be divided into:
• Routing decisions, where a route for each aircraft has to be chosen in order to balance the use of TCA resources.
• Scheduling decisions, where routes are considered fixed,and feasible aircraft scheduling solutions have to be determined.
In practice, the two decisions are taken simultaneously.
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selectediskhif
selectedisjiifx
AkhjiMxwtt
xMwttFjiwtt
xtf
hkij
hkijhkhk
hkijijij
ijij
),(0
),(1
),(),,(()1(
),(
),(min
,
,
,
MILP (Mixed-Integer Linear Programming) modelMILP (Mixed-Integer Linear Programming) modelFIXED AIRCRAFT ROUTESFIXED AIRCRAFT ROUTES
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otherwise
selectedissaircraftofrrouteify
selectediskhif
selectedisjiifx
AkhjiyMyMMxwtt
yMyMxMwttsrFjiyMwtt
nasy
yxtf
rshkij
uvrshkijhkhk
uvrshkijijij
rsrsijij
ns
rrs
0
1
),(0
),(1
),(),,(()1()1(
)1()1()1(,,),()1(
,...,11
),,(min
,
,
,
1
ns: number of routes of aircraft s na: number of aircraft
MILP (Mixed-Integer Linear Programming) modelMILP (Mixed-Integer Linear Programming) modelFLEXIBLE AIRCRAFT ROUTESFLEXIBLE AIRCRAFT ROUTES
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Rolling Horizon (RH) approachRolling Horizon (RH) approach
time
Time horizon T1
Time horizon T2
Time horizon T3
Roll period
Roll period
Length of the overall traffic prediction horizon
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Junior ConsultingDipartimento di Ingegneria
CommonGlide Path
RunwaysHolding CirclesAir Segments
8
16
173
SRN
1
TOR
MBR
2 6
4 10
11
12
15
7
513
14
RWY 35L
RWY 35R
9
AA
BB
RH:RH:Stage 1Stage 1
A1 A4 A15A10 A13 AOUTA16
0 *
B3 B8 B15B12 B14 BOUTB17
αA = 10
αB = 0
βA = -10
βA = 0
Time horizon T1 [0;15]
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A1 A4 A15A10 A13 AOUTA16
0 *B15B14 BOUTB17
αA = 10
αB = 5βA = -10
βB = -5
COUTC17
βC = -17
αC = 17
CommonGlide Path
RunwaysHolding CirclesAir Segments
8
16
173
SRN
1
TOR
MBR
2 6
4 10
11
12
15
7
513
14
RWY 35L
RWY 35R
9
AA
B
B
RH:RH:Stage 2Stage 2
Roll Period = 5Time horizon T2 [5;20]
Observation: At this stage the release time of A and C can be updated dynamically if updated entry times are known
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C
C
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Decision Support System based Decision Support System based on the Rolling Horizon Approachon the Rolling Horizon Approach
Instance Generator
Feasible Solution
Set new roll period
Aircraft not fully processed
Single StageSolver
Aircraft entry times (dynamic information)
XML file
Airport ResourcesAircraft TimesAircraft Routes
Time HorizonRoll period
(if any)
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Single Stage Solver: Single Stage Solver: AGLIBRARYAGLIBRARY
Aircraft Scheduling
Module
Stopping CriteriaReached?
AircraftRerouting
Module
New Schedule
No
Yes
New Routes
ReturnBest Solution Found
Heuristics (e.g. FCFS, AGH, JGH) Branch and Bound (BB)
Tabu Search (TS)
Airport ResourcesAircraft TimesAircraft RoutesTime HorizonRoll period
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D’Ariano 2008
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Junior ConsultingDipartimento di Ingegneria
IntroductionModeling a Terminal Maneuvering Area Solution Framework and AlgorithmsComputational ExperimentsConclusions and Ongoing Research
Presentation outlinePresentation outline
This work was partially supported by the Italian Ministry of Research, project FIRB “Advanced tracking system in intermodal freight transportation”.
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Processor Intel i7 (2.84 GHz), 8 GB Ram
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3-ho
ur h
oriz
onCentralized vs Rolling HorizonCentralized vs Rolling Horizon
[20 instances]
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Static/Dynamic Case: BB vs FCFSStatic/Dynamic Case: BB vs FCFS
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1-hour horizon
[20 instances]
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Junior ConsultingDipartimento di Ingegneria
IntroductionModeling a Terminal Control Area Solution Framework and AlgorithmsComputational ExperimentsConclusions and Ongoing Research
Presentation outlinePresentation outline
This work was partially supported by the Italian Ministry of Research, project FIRB “Advanced tracking system in intermodal freight transportation”.
35
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AchievementsAchievements• Detailed ASP models have been investigated for MXP and FCO;
• The computational experiments proved the effectiveness of our rolling horizon approach compared to a centralized approach;
• Optimization algorithms outperforms simple rules, both for static and dynamic cases, in terms of delay and travel time minimization;
• The BB-based rolling horizon approach solves the one-hour instances quickly.
[email protected]@dia.uniroma3.it
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Further research directionsFurther research directions
• Development of detailed models for the coordination & real-time optimization of en-route, approach and TCA traffic management
[email protected]@dia.uniroma3.it
• Transformative: Practical realization of integrated (closed-loop) intelligent decision support systems at traffic control centers
• Study of multiple criteria for aircraft traffic control at busy TCAs (e.g. delay, priority, fairness, environmental and other cost factors)
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• Evaluation of aircraft rescheduling and rerouting approaches for optimal decision making in case of various traffic disturbances