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

1. 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

2. Presentationoutline • Introduction • Modeling a Terminal Control Area • Solution Framework and Algorithms • Computational Experiments • Conclusions and Ongoing Research This work was partially supported by the Italian Ministry of Research, project FIRB “Advanced tracking system in intermodal freight transportation”.

3. Air Traffic Control (ATC) Air traffic control must ensure safe, ordered and rapid transit of aircraft on the ground and in the air segments. With the increase in air traffic [*], aviation authorities are seeking methods (i) to better usethe existing airport infrastructure, and (ii) to better manageaircraft movements in the vicinity of airports during operations. [*] Source: EUROCONTROL Short-term forecast 2009

4. Status 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 representationof 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).

5. Literature: Aircraft Scheduling Problem (ASP) Chris Potts et al. 2011 Basic (e.g. Bertsimas, Lulli, Odoni ) Static (e.g. Dear, Hu, Chen) Existing Approaches Dynamic (e.g. Beasley, Ernst) Detailed (e.g. Bianco, Dell’Olmo, Giordani)

6. Literature: 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. 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.

7. Our 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: how does the traffic control system react when disturbances arise, when and how is it more convenient to reschedule aircraft in the TCA, which algorithm performs best in terms of delay and travel time minimization, which algorithm is the less sensitive to disturbances.

8. Presentationoutline • Introduction • Modeling a Terminal Control Area • Solution Framework and Algorithms • Computational Experiments • Conclusions and Ongoing Research This work was partially supported by the Italian Ministry of Research, project FIRB “Advanced tracking system in intermodal freight transportation”.

9. MXP TCA : (MILAN, ITALY) FCO TCA : (ROME, ITALY)

10. The Alternative Graph (AG) Model Mascis & Pacciarelli 2002 • 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.

11. 1 TOR 2 MBR 3 SRN Air Segments Common Glide Path AG Model Holding Circles Runways A 4 10 13 16 9 5 RWY 35L 6 15 11 7 14 17 12 8 RWY 35R A1 αA 0 * Release date αA (αA = expected entry time of aircraft A)

12. 1 TOR 2 MBR 3 SRN AG Model Holding Circles Air Segments Common Glide Path Runways A 4 10 13 16 9 5 RWY 35L 6 15 11 7 14 17 12 8 RWY 35R A1 αA βA 0 * Entry due date βA ( βA = - αA )

13. 1 TOR 2 MBR 3 SRN AG Model Holding Circles Air Segments Common Glide Path Runways A 4 10 13 16 9 5 RWY 35L 6 15 11 7 14 17 12 8 RWY 35R δ 0 A1 A4 -δ αA βA 0 0 * (A4, A1) No holding circle (holding time = 0) (A1, A4) Yes holding circle (holding time = δ)

14. 1 TOR 2 MBR 3 SRN AG Model Holding Circles Common Glide Path Air Segments Runways A 4 10 13 16 9 5 RWY 35L 6 15 11 7 14 17 12 8 RWY 35R min A1 A4 A10 - max αA βA 0 * Time window for the travel time in each air segment [min travel time; max travel time]

15. 1 TOR 2 MBR 3 SRN AG Model Common Glide Path Holding Circles Air Segments Runways A A 4 10 13 16 9 5 RWY 35L 6 15 11 7 14 17 12 8 RWY 35R A1 A4 A10 A13 A15 A16 AOUT γA αA βA 0 * Exit due date γA (γA = - planned landing time)

16. 1 TOR 2 MBR 3 SRN AG Model Holding Circles Common Glide Path Air Segments Runways A A 4 10 13 16 9 5 RWY 35L Potential conflict on resource 15 ! 6 15 11 7 14 B 17 12 8 B RWY 35R A1 A4 A10 A13 A15 A16 AOUT γA αA βA 0 * βB Aircraft ordering problem between A and B on the common glide path (resource 15) : Longitudinal and diagonal distances have to be respected γB αB B3 B8 B12 B14 B15 B17 BOUT

17. 1 TOR 2 MBR 3 SRN AG Model Common Glide Path Holding Circles Air Segments Runways A A 4 10 Aircraft ordering problem between B and C for the runway (resource 17): This is a no-store resource! 13 16 9 5 RWY 35L 6 15 11 C 7 14 B 17 C 12 8 B RWY 35R A1 A4 A10 A13 A15 A16 AOUT γA αA βA 0 * βB γB αB B3 B8 B12 B14 B15 B17 BOUT αC C17 COUT γC

18. Presentation outline • Introduction • Modeling a Terminal Control Area • Solution Framework and Algorithms • Computational Experiments • Conclusions and Ongoing Research This work was partially supported by the Italian Ministry of Research, project FIRB “Advanced tracking system in intermodal freight transportation”.

19. Developing a decisionsupporttool From 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. 19

20. MILP (Mixed-Integer Linear Programming) model FIXED AIRCRAFT ROUTES 20

21. MILP (Mixed-Integer Linear Programming) model FLEXIBLE AIRCRAFT ROUTES ns: number of routes of aircraft s na: number of aircraft 21

22. RollingHorizon (RH) approach Time horizon T1 Roll period Time horizon T2 Roll period Time horizon T3 time Length of the overall traffic prediction horizon

23. 1 TOR 2 MBR 3 SRN Air Segments Common Glide Path Holding Circles Runways A A RH: Stage 1 4 10 13 16 9 5 RWY 35L 6 15 11 7 Time horizon T1 [0;15] 14 B 17 12 8 B RWY 35R A1 A4 A10 A13 A15 A16 AOUT αA = 10 βA = -10 0 * βA = 0 αB = 0 B3 B8 B12 B14 B15 B17 BOUT

24. 1 TOR 2 MBR 3 SRN RH: Stage 2 Air Segments Common Glide Path Holding Circles Runways A A 4 10 13 16 9 5 RWY 35L 6 15 11 C Roll Period = 5 Time horizon T2 [5;20] 7 14 C B 17 12 8 RWY 35R B A1 A4 A10 A13 A15 A16 AOUT αA = 10 βA = -10 αB = 5 0 * B14 B15 B17 BOUT αC = 17 βB = -5 C17 COUT βC = -17 Observation: At this stage the release time of A and C can be updated dynamically if updated entry times are known

25. Aircraft entry times (dynamic information) Airport Resources Feasible Solution Aircraft Times (if any) DecisionSupport System based on the RollingHorizonApproach Aircraft Routes XML file Instance Single Stage Generator Solver Time Horizon Roll period Aircraft not fully processed Set new roll period

26. Return Yes New Best Solution Schedule Aircraft Found Stopping Criteria Scheduling Reached? Module No New Aircraft Routes Rerouting Module Single Stage Solver: AGLIBRARY D’Ariano 2008 Heuristics (e.g. FCFS, AGH, JGH) Branch and Bound (BB) Airport Resources Aircraft Times Aircraft Routes Time Horizon Roll period Tabu Search (TS)

27. Presentation outline • Introduction • Modeling a Terminal Maneuvering Area • Solution Framework and Algorithms • Computational Experiments • Conclusions and Ongoing Research Processor Intel i7 (2.84 GHz), 8 GB Ram This work was partially supported by the Italian Ministry of Research, project FIRB “Advanced tracking system in intermodal freight transportation”.

28. Centralized vs RollingHorizon [20 instances] 3-hour horizon

29. Static/Dynamic Case: BB vs FCFS 1-hour horizon [20 instances]

30. Presentationoutline • Introduction • Modeling a Terminal Control Area • Solution Framework and Algorithms • Computational Experiments • Conclusions and Ongoing Research This work was partially supported by the Italian Ministry of Research, project FIRB “Advanced tracking system in intermodal freight transportation”.

31. Achievements • 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. a.dariano@dia.uniroma3.it

32. Furtherresearchdirections • Evaluation of aircraft rescheduling and rerouting approaches for optimal decision making in case of various traffic disturbances • Study of multiple criteria for aircraft traffic control at busy TCAs • (e.g. delay, priority, fairness, environmental and other cost factors) • Development of detailed models for the coordination & real-time optimization of en-route, approach and TCA traffic management • Transformative: Practical realization of integrated (closed-loop) intelligent decision support systems at traffic control centers a.dariano@dia.uniroma3.it