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D'ARIANO ATRS 2016

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Andrea D'Ariano

This document summarizes research on scheduling models for optimal aircraft traffic control at busy airports. It presents a new approach for modeling terminal control areas that includes accurate modeling of traffic regulations at runways and airways. Mixed integer linear programming formulations are developed to represent the scheduling problem and alternative objective functions are investigated, including maximum and average delays, equity considerations, and priority tardiness. Computational experiments applying the models to real traffic data from Italian airports demonstrate trade-offs between different objective functions. The work contributes new microscopic models for aircraft scheduling in terminal control areas.

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Scheduling models for optimal aircraft traffic control at busy airports: tardiness, priorities, equity and violations considerations Dipartimento di Ingegneria Andrea D’Ariano, ROMA TRE University, Rome, Italy 124/11/2016
Junior ConsultingDipartimento di Ingegneria Introduction Modeling a Terminal Control Area MILP formulations Scheduling models for optimal aircraft traffic control at busy airports: tardiness, priorities, equity and violations considerations MILP formulations Computational experiments Conclusions This work has been recently accepted for publication in the journal OMEGA : Reference DOI: 10.1016/j.omega.2016.04.003 2
Junior ConsultingDipartimento di Ingegneria Air Traffic Control (ATC)Air Traffic Control (ATC) An efficient control of air traffic must ensure safe, ordered and rapid transit of aircraft on the ground and in the air resources. With the increase in air traffic [*], aviation authorities are seeking methods (i) to better use the [*] Source: IATA 2014 methods (i) to better use the existing airport infrastructure, and (ii) to better manage aircraft movements in the vicinity of airports during operations. 3
Junior ConsultingDipartimento di Ingegneria StatusStatus ofof thethe currentcurrent ATCATC practisepractise • 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). 4
Junior ConsultingDipartimento di Ingegneria Literature: Aircraft Scheduling Problem (ASP)Literature: Aircraft Scheduling Problem (ASP) Terminal Control Area (TCA)Terminal Control Area (TCA) Detailed BasicExisting Approaches Dynamic Static 5
Junior ConsultingDipartimento di Ingegneria Literature: Research needsLiterature: Research needs Aircraft Scheduling Problem 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. We present a new approach that includes both accurate modelling of traffic regulations at runways and airways. 6 This approach has already been applied to successully control railway traffic for metro lines and railway networks.
Junior ConsultingDipartimento di Ingegneria Our approach for TCAsOur approach for TCAs Implementation and testing of: • Detailed ASP-TCA models: incorporating safety rules at air segments, runways and holding circlesair segments, runways and holding circles • Alternative objective functions: maximum versus average delays, delayed aircraft (violations), aircraft equity, throughput (completion time), priority tardiness • Real-time traffic management instances: Roma Fiumicino (FCO) and Milano Malpensa (MXP) airports 7
Junior ConsultingDipartimento di Ingegneria Introduction Modeling a Terminal Control Area MILP formulations Scheduling models for optimal aircraft traffic control at busy airports: tardiness, priorities, equity and violations considerations MILP formulations Computational experiments Conclusions 8 This work has been recently accepted for publication in the journal OMEGA : Reference DOI: 10.1016/j.omega.2016.04.003
Junior ConsultingDipartimento di Ingegneria 3 HOLDING CIRCLES SEVERAL AIR SEGMENTS 1 COMMON GLIDE PATH 3 RUNWAYS3 RUNWAYS
Junior ConsultingDipartimento di Ingegneria ASPASP ModelModel:: AlternativeAlternative GraphGraph (AG)(AG) Air Segments Common Glide Path RunwaysHolding Circles 8 16 17 3 SRN 1 TOR MBR 2 6 4 10 11 12 15 7 5 13 14 RWY 16R RWY 16L 9 A [Pacciarelli EJOR 2002] RWY 25 A1 tA1 10 release date αA (w0, A1 = αA = expected aircraft entry time) Fixed constraints tA1 = t0 + w0, A1 A1 0 αA t0
Junior ConsultingDipartimento di Ingegneria AGAG ModelModel Air Segments Common Glide Path RunwaysHolding Circles 8 16 17 3 SRN 1 TOR MBR 2 6 4 10 11 12 15 7 5 13 14 RWY 16R RWY 16L 9 A A1 RWY 25 tA1 entry due date βA (wA1,n = βA = - αA ) A1 0 n αA βA 11 tn = tA1 + wA1,n tn
Junior ConsultingDipartimento di Ingegneria AGAG ModelModel Air Segments Common Glide Path RunwaysHolding Circles 8 16 17 3 SRN 1 TOR MBR 2 6 4 10 11 12 15 7 5 13 14 RWY 16R RWY 16L 9 A A1 A4 δ 0 -δ RWY 25 dotted arc (A4, A1) No holding circle dotted arc (A1, A4) Yes holding circle (δ = holding time) A1 A4 0 n αA βA -δ 0 12 Alternative constraints
Junior ConsultingDipartimento di Ingegneria AGAG ModelModel A1 A4 A10 min Air Segments Common Glide Path RunwaysHolding Circles 8 16 17 3 SRN 1 TOR MBR 2 6 4 10 11 12 15 7 5 13 14 RWY 16R RWY 16L 9 A RWY 25 A1 A4 A10 0 n αA βA - max Time window for the travel time in each air segment [min travel time; max travel time] 13
Junior ConsultingDipartimento di Ingegneria Common Glide Path RunwaysHolding Circles Air Segments 8 16 17 3 SRN 1 TOR MBR 2 6 4 10 11 12 15 7 5 13 14 RWY 16R RWY 16L 9 A A AGAG ModelModel A1 A4 A15A10 A13 AOUTA16 RWY 25Aircraft routing: A1-A4-A10-A13-A15-A16 A1 A4 A15A10 A13 AOUTA16 0 n αA βA γA exit due date γA (γA = - planned landing time) 14
Junior ConsultingDipartimento di Ingegneria Common Glide Path RunwaysHolding Circles Air Segments 8 16 17 3 SRN 1 TOR MBR 2 6 4 10 11 12 15 7 5 13 14 RWY 16R RWY 16L 9 A A B B AGAG ModelModel A1 A4 A15A10 A13 AOUTA16 Potential conflict between A and B on the common glide path (resource 15) ! RWY 25 A1 A4 A15A10 A13 AOUTA16 0 n B3 B8 B15B12 B14 BOUTB17 αA αB βA γA βB γB Aircraft ordering problem between A and B for the common glide path (resource 15) : longitudinal and diagonal distances must be respected 15
Junior ConsultingDipartimento di Ingegneria Common Glide Path RunwaysHolding Circles Air Segments 8 16 17 3 SRN 1 TOR MBR 2 6 4 10 11 12 15 7 5 13 14 RWY 16R RWY 16L 9 A A B B CC AGAG ModelModel A1 A4 A15A10 A13 AOUTA16 α γ Potential conflict between C and B on a runway (resource 17) ! RWY 25 0 n B3 B8 B15B12 B14 BOUTB17 αA αB βA γA βB γB COUTC17 γC αC 16
Junior ConsultingDipartimento di Ingegneria Introduction Modeling a Terminal Control Area MILP formulations Scheduling models for optimal aircraft traffic control at busy airports: tardiness, priorities, equity and violations considerations MILP formulations Computational experiments Conclusions 17 This work has been recently accepted for publication in the journal OMEGA : Reference DOI: 10.1016/j.omega.2016.04.003
Junior ConsultingDipartimento di Ingegneria AGAG viewedviewed asas a MILP (a MILP (MixedMixed--IntegerInteger LinearLinear ProgramProgram))      ∈∀ −+≥ −−+≥ ∈∀+≥ Akhji Mxwtt xMwtt Fmlwtt xtf hkijhkhk hkijijij lmlm ),(),,(( )1( ),( ),(min , ,C = with m ≠ n 18 • Fixed constraints in F model feasible timing for each aircraft on its specific route, plus α, β, γ constraints on the entrance and exit times. • Alternative constraints in A represent the ordering decision between aircraft at air segments and runways, plus holding circle decisions.    =  −+≥ selectediskhif selectedisjiif x Mxwtt hkij hkijhkhk ),(0 ),(1 , ,
Junior ConsultingDipartimento di Ingegneria InvestigatedInvestigated objectiveobjective functionsfunctions Average Tardiness Priority TardinessPriority Equity Maximum Tardiness 19 Max Completion Avg Completion Tardy Jobs P
Junior ConsultingDipartimento di Ingegneria Introduction Modeling a Terminal Control Area MILP formulations PresentationPresentation outlineoutline MILP formulations Computational experiments Conclusions 20 This work has been recently accepted for publication in the journal OMEGA : Reference DOI: 10.1016/j.omega.2016.04.003
Junior ConsultingDipartimento di Ingegneria DescriptionDescription ofof the testthe test casescases 21 Each row presents 20 disturbed scenarios (ASP instances); Entrance delays are randomly generated with various distributions; Unavoidable delays cannot be recovered by aircraft rescheduling; ASP solutions are computed by means of CPLEX MIP solver 12.0.
Junior ConsultingDipartimento di Ingegneria AA practicalpractical schedulingscheduling rulerule Optimizing an objective andOptimizing an objective and looking at the other objectiveslooking at the other objectives 22
Junior ConsultingDipartimento di Ingegneria Optimizing an objective andOptimizing an objective and looking at the other objectiveslooking at the other objectives 23
Junior ConsultingDipartimento di Ingegneria AA combinedcombined approachapproach Optimizing an objective andOptimizing an objective and looking at the other objectiveslooking at the other objectives 24
Junior ConsultingDipartimento di Ingegneria Introduction Modeling a Terminal Control Area MILP formulations Scheduling models for optimal aircraft traffic control at busy airports: tardiness, priorities, equity and violations considerations MILP formulations Computational experiments Conclusions 25 This work has been recently accepted for publication in the journal OMEGA : Reference DOI: 10.1016/j.omega.2016.04.003
Junior ConsultingDipartimento di Ingegneria AchievementsAchievements • Microscopic ASP-TCA optimization models are proposed. • Various objective functions and approaches are investigated. • Computational results for major Italian TCAs demonstrate the existence of relevant gaps between the objective functions.existence of relevant gaps between the objective functions. • Combining the various objectives offers good trade-off solutions. dariano@ing.uniroma3.itdariano@ing.uniroma3.it

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D'ARIANO ATRS 2016

  • 1. Scheduling models for optimal aircraft traffic control at busy airports: tardiness, priorities, equity and violations considerations Dipartimento di Ingegneria Andrea D’Ariano, ROMA TRE University, Rome, Italy 124/11/2016
  • 2. Junior ConsultingDipartimento di Ingegneria Introduction Modeling a Terminal Control Area MILP formulations Scheduling models for optimal aircraft traffic control at busy airports: tardiness, priorities, equity and violations considerations MILP formulations Computational experiments Conclusions This work has been recently accepted for publication in the journal OMEGA : Reference DOI: 10.1016/j.omega.2016.04.003 2
  • 3. Junior ConsultingDipartimento di Ingegneria Air Traffic Control (ATC)Air Traffic Control (ATC) An efficient control of air traffic must ensure safe, ordered and rapid transit of aircraft on the ground and in the air resources. With the increase in air traffic [*], aviation authorities are seeking methods (i) to better use the [*] Source: IATA 2014 methods (i) to better use the existing airport infrastructure, and (ii) to better manage aircraft movements in the vicinity of airports during operations. 3
  • 4. Junior ConsultingDipartimento di Ingegneria StatusStatus ofof thethe currentcurrent ATCATC practisepractise • 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). 4
  • 5. Junior ConsultingDipartimento di Ingegneria Literature: Aircraft Scheduling Problem (ASP)Literature: Aircraft Scheduling Problem (ASP) Terminal Control Area (TCA)Terminal Control Area (TCA) Detailed BasicExisting Approaches Dynamic Static 5
  • 6. Junior ConsultingDipartimento di Ingegneria Literature: Research needsLiterature: Research needs Aircraft Scheduling Problem 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. We present a new approach that includes both accurate modelling of traffic regulations at runways and airways. 6 This approach has already been applied to successully control railway traffic for metro lines and railway networks.
  • 7. Junior ConsultingDipartimento di Ingegneria Our approach for TCAsOur approach for TCAs Implementation and testing of: • Detailed ASP-TCA models: incorporating safety rules at air segments, runways and holding circlesair segments, runways and holding circles • Alternative objective functions: maximum versus average delays, delayed aircraft (violations), aircraft equity, throughput (completion time), priority tardiness • Real-time traffic management instances: Roma Fiumicino (FCO) and Milano Malpensa (MXP) airports 7
  • 8. Junior ConsultingDipartimento di Ingegneria Introduction Modeling a Terminal Control Area MILP formulations Scheduling models for optimal aircraft traffic control at busy airports: tardiness, priorities, equity and violations considerations MILP formulations Computational experiments Conclusions 8 This work has been recently accepted for publication in the journal OMEGA : Reference DOI: 10.1016/j.omega.2016.04.003
  • 9. Junior ConsultingDipartimento di Ingegneria 3 HOLDING CIRCLES SEVERAL AIR SEGMENTS 1 COMMON GLIDE PATH 3 RUNWAYS3 RUNWAYS
  • 10. Junior ConsultingDipartimento di Ingegneria ASPASP ModelModel:: AlternativeAlternative GraphGraph (AG)(AG) Air Segments Common Glide Path RunwaysHolding Circles 8 16 17 3 SRN 1 TOR MBR 2 6 4 10 11 12 15 7 5 13 14 RWY 16R RWY 16L 9 A [Pacciarelli EJOR 2002] RWY 25 A1 tA1 10 release date αA (w0, A1 = αA = expected aircraft entry time) Fixed constraints tA1 = t0 + w0, A1 A1 0 αA t0
  • 11. Junior ConsultingDipartimento di Ingegneria AGAG ModelModel Air Segments Common Glide Path RunwaysHolding Circles 8 16 17 3 SRN 1 TOR MBR 2 6 4 10 11 12 15 7 5 13 14 RWY 16R RWY 16L 9 A A1 RWY 25 tA1 entry due date βA (wA1,n = βA = - αA ) A1 0 n αA βA 11 tn = tA1 + wA1,n tn
  • 12. Junior ConsultingDipartimento di Ingegneria AGAG ModelModel Air Segments Common Glide Path RunwaysHolding Circles 8 16 17 3 SRN 1 TOR MBR 2 6 4 10 11 12 15 7 5 13 14 RWY 16R RWY 16L 9 A A1 A4 δ 0 -δ RWY 25 dotted arc (A4, A1) No holding circle dotted arc (A1, A4) Yes holding circle (δ = holding time) A1 A4 0 n αA βA -δ 0 12 Alternative constraints
  • 13. Junior ConsultingDipartimento di Ingegneria AGAG ModelModel A1 A4 A10 min Air Segments Common Glide Path RunwaysHolding Circles 8 16 17 3 SRN 1 TOR MBR 2 6 4 10 11 12 15 7 5 13 14 RWY 16R RWY 16L 9 A RWY 25 A1 A4 A10 0 n αA βA - max Time window for the travel time in each air segment [min travel time; max travel time] 13
  • 14. Junior ConsultingDipartimento di Ingegneria Common Glide Path RunwaysHolding Circles Air Segments 8 16 17 3 SRN 1 TOR MBR 2 6 4 10 11 12 15 7 5 13 14 RWY 16R RWY 16L 9 A A AGAG ModelModel A1 A4 A15A10 A13 AOUTA16 RWY 25Aircraft routing: A1-A4-A10-A13-A15-A16 A1 A4 A15A10 A13 AOUTA16 0 n αA βA γA exit due date γA (γA = - planned landing time) 14
  • 15. Junior ConsultingDipartimento di Ingegneria Common Glide Path RunwaysHolding Circles Air Segments 8 16 17 3 SRN 1 TOR MBR 2 6 4 10 11 12 15 7 5 13 14 RWY 16R RWY 16L 9 A A B B AGAG ModelModel A1 A4 A15A10 A13 AOUTA16 Potential conflict between A and B on the common glide path (resource 15) ! RWY 25 A1 A4 A15A10 A13 AOUTA16 0 n B3 B8 B15B12 B14 BOUTB17 αA αB βA γA βB γB Aircraft ordering problem between A and B for the common glide path (resource 15) : longitudinal and diagonal distances must be respected 15
  • 16. Junior ConsultingDipartimento di Ingegneria Common Glide Path RunwaysHolding Circles Air Segments 8 16 17 3 SRN 1 TOR MBR 2 6 4 10 11 12 15 7 5 13 14 RWY 16R RWY 16L 9 A A B B CC AGAG ModelModel A1 A4 A15A10 A13 AOUTA16 α γ Potential conflict between C and B on a runway (resource 17) ! RWY 25 0 n B3 B8 B15B12 B14 BOUTB17 αA αB βA γA βB γB COUTC17 γC αC 16
  • 17. Junior ConsultingDipartimento di Ingegneria Introduction Modeling a Terminal Control Area MILP formulations Scheduling models for optimal aircraft traffic control at busy airports: tardiness, priorities, equity and violations considerations MILP formulations Computational experiments Conclusions 17 This work has been recently accepted for publication in the journal OMEGA : Reference DOI: 10.1016/j.omega.2016.04.003
  • 18. Junior ConsultingDipartimento di Ingegneria AGAG viewedviewed asas a MILP (a MILP (MixedMixed--IntegerInteger LinearLinear ProgramProgram))      ∈∀ −+≥ −−+≥ ∈∀+≥ Akhji Mxwtt xMwtt Fmlwtt xtf hkijhkhk hkijijij lmlm ),(),,(( )1( ),( ),(min , ,C = with m ≠ n 18 • Fixed constraints in F model feasible timing for each aircraft on its specific route, plus α, β, γ constraints on the entrance and exit times. • Alternative constraints in A represent the ordering decision between aircraft at air segments and runways, plus holding circle decisions.    =  −+≥ selectediskhif selectedisjiif x Mxwtt hkij hkijhkhk ),(0 ),(1 , ,
  • 19. Junior ConsultingDipartimento di Ingegneria InvestigatedInvestigated objectiveobjective functionsfunctions Average Tardiness Priority TardinessPriority Equity Maximum Tardiness 19 Max Completion Avg Completion Tardy Jobs P
  • 20. Junior ConsultingDipartimento di Ingegneria Introduction Modeling a Terminal Control Area MILP formulations PresentationPresentation outlineoutline MILP formulations Computational experiments Conclusions 20 This work has been recently accepted for publication in the journal OMEGA : Reference DOI: 10.1016/j.omega.2016.04.003
  • 21. Junior ConsultingDipartimento di Ingegneria DescriptionDescription ofof the testthe test casescases 21 Each row presents 20 disturbed scenarios (ASP instances); Entrance delays are randomly generated with various distributions; Unavoidable delays cannot be recovered by aircraft rescheduling; ASP solutions are computed by means of CPLEX MIP solver 12.0.
  • 22. Junior ConsultingDipartimento di Ingegneria AA practicalpractical schedulingscheduling rulerule Optimizing an objective andOptimizing an objective and looking at the other objectiveslooking at the other objectives 22
  • 23. Junior ConsultingDipartimento di Ingegneria Optimizing an objective andOptimizing an objective and looking at the other objectiveslooking at the other objectives 23
  • 24. Junior ConsultingDipartimento di Ingegneria AA combinedcombined approachapproach Optimizing an objective andOptimizing an objective and looking at the other objectiveslooking at the other objectives 24
  • 25. Junior ConsultingDipartimento di Ingegneria Introduction Modeling a Terminal Control Area MILP formulations Scheduling models for optimal aircraft traffic control at busy airports: tardiness, priorities, equity and violations considerations MILP formulations Computational experiments Conclusions 25 This work has been recently accepted for publication in the journal OMEGA : Reference DOI: 10.1016/j.omega.2016.04.003
  • 26. Junior ConsultingDipartimento di Ingegneria AchievementsAchievements • Microscopic ASP-TCA optimization models are proposed. • Various objective functions and approaches are investigated. • Computational results for major Italian TCAs demonstrate the existence of relevant gaps between the objective functions.existence of relevant gaps between the objective functions. • Combining the various objectives offers good trade-off solutions. dariano@ing.uniroma3.itdariano@ing.uniroma3.it