Dipartimento di Ingegneria Rolling Horizon Approach For Aircraft Scheduling In The Terminal Control Area Of Busy Airports Andrea DAriano, ROMA TRE University.

Dipartimento di Ingegneria

Rolling Horizon Approach For Aircraft Scheduling In The Terminal Control Area Of Busy Airports

Andrea D’Ariano, ROMA TRE University1ISTTT, 10/04/23

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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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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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.


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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8


9

MXP TCA :(MILAN, ITALY)

FCO TCA :(ROME, ITALY)


• 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


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

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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

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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


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



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

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AAAG Model AG Model

A1 A4 A15A10 A13 AOUTA16

0 *

αA

βA

γA

Exit due date γA

(γA = - planned landing time)

15




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


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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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


0 *


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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18


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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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20

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),(0

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MILP (Mixed-Integer Linear Programming) modelMILP (Mixed-Integer Linear Programming) modelFIXED AIRCRAFT ROUTESFIXED AIRCRAFT ROUTES


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otherwise

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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


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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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


0 *


αA = 10

αB = 0

βA = -10

βA = 0

Time horizon T1 [0;15]

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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


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


IntroductionModeling a Terminal Maneuvering Area Solution Framework and AlgorithmsComputational ExperimentsConclusions and Ongoing Research



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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]


Static/Dynamic Case: BB vs FCFSStatic/Dynamic Case: BB vs FCFS

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1-hour horizon

[20 instances]





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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


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

Dipartimento di Ingegneria Rolling Horizon Approach For Aircraft Scheduling In The Terminal Control Area Of Busy Airports Andrea DAriano, ROMA TRE University.

Documents

junior consulting dipartimento

ingegneria literature

max slide

ingegneria introduction

ingegneria a4

ingegneria status

ingegneria ag model

ingegneria ag model