This study considers the terminal traffic flow problem and aims to minimize the total fuel consumed in extended terminal maneuvering areas (E-TMA) using a mixed-integer linear programming model. To reveal the benefits of the E-TMA concept, the exact solution solver is selected to determine optimal aircraft sequencing. Furthermore, the proposed model applies separation and sequencing maneuvers, including the point merge system (PMS), vector maneuvers (VM), and en route speed reduction maneuvers. An innovative mathematical model is presented for this problem. In this study, the proposed model is compared with two other approaches. The first uses VM and PMS without changing the initial sequencing. The second utilizes the VM and PMS maneuvers. Unlike the first approach, it searches for the optimum aircraft arrival sequencing. The results demonstrate that the proposed model could reduce total fuel consumption by 9.45% and 1.95% compared with the other two approaches.
FavennecB.VergneF.ZeghalK.Point Merge Integration of Arrival Flows Enabling Extensive RNAV Application and Continuous Descent - Operational Services and Environment Definition. EUROCONTROL Experimental Centre, Brétigny-sur-Orge, France, Version 2010, pp. 1–89.
2.
ErkelensL.Research into New Noise Abatement Procedures for the 21st Century. In AIAA Guidance, Navigation, and Control Conference and Exhibit, August 14–17, 2000. Denver, CO. https://doi.org/10.2514/6.2000-4474.
3.
WarrenA.TongK. O.Development of Continuous Descent Approach Concepts for Noise Abatement [ATC]. Proc., 21st Digital Avionics Systems Conference, Vol. 1, New York, IEEE, 2002.
4.
ClarkeJ. P. B.HoN. T.RenL.BrownJ. A.ElmerK. R.TongK. O.WatJ. K.Continuous Descent Approach: Design and Flight Test for Louisville International Airport. Journal of Aircraft, Vol. 41, No. 5, 2004, pp. 1054–1066.
5.
UsanmazO.SınarG.SahinO.An Assessment of a Proposed Arrival Route Model in Terminal Airspace. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, Vol. 233, No. 11, 2019, pp. 4032–4038.
6.
BoursierL.FavennecB.HoffmanE.TrzmielA.VergneF.ZeghalK.Merging Arrival Flows Without Heading Instructions. 7th USA/Europe Air Traffic Management R&D Seminar, Barcelona, Spain, 2007.
7.
TielrooijM.BorstC.Van PaassenM. M.MulderM.Predicting Arrival Time Uncertainty from Actual Flight Information. Proc., 11th USA/Europe Air Traffic Management Research and Development Seminar, Lisbon, Portugal, 2015, pp. 577–586.
8.
BiancoL.Dell’OlmoP.GiordaniS.Scheduling Models for Air Traffic Control in Terminal Areas. Journal of Scheduling, Vol. 9, No. 3, 2006, pp. 223–253.
NgK. K. H.LeeC. K.ChanF. T.LvY.Review on Meta-Heuristics Approaches for Airside Operation Research. Applied Soft Computing, Vol. 66, 2018, pp. 104–133.
11.
SamàM.D’ArianoA.PalagachevK.GerdtsM.Integration Methods for Aircraft Scheduling and Trajectory Optimization at a Busy Terminal Manoeuvring Area. OR Spectrum, Vol. 41, No. 3, 2019, pp. 641–681.
12.
ZhangJ.ZhaoP.ZhangY.DaiX.SuiD.Criteria Selection and Multi-Objective Optimization of Aircraft Landing Problem. Journal of Air Transport Management, Vol. 82, 2020, p. 101734.
13.
NgK. K. H.LeeC. K.ChanF. T.ChenC. H.QinY.A Two-Stage Robust Optimisation for Terminal Traffic Flow Problem. Applied Soft Computing, Vol. 89, 2020, p. 106048.
DönmezK.ÇetekC.KayaO.Aircraft Sequencing and Scheduling in Parallel-Point Merge Systems for Multiple Parallel Runways. Transportation Research Record: Journal of the Transportation Research Board, 2021. 1–17.
16.
SamaM.D’ArianoA.D’ArianoP.PacciarelliD.Optimal Aircraft Scheduling and Routing at a Terminal Control Area During Disturbances. Transportation Research Part C: Emerging Technologies, Vol. 47, 2014, pp. 61–85.
17.
SamaM.D’ArianoA.D’ArianoP.PacciarelliD.Scheduling Models for Optimal Aircraft Traffic Control at Busy Airports: Tardiness, Priorities, Equity and Violations Considerations. Omega, Vol. 67, 2017, pp. 81–98.
18.
LeeS.HongY.KimY.Optimal Scheduling Algorithm in Point Merge System Including Holding Pattern Based on Mixed-Integer Linear Programming. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, Vol. 234, No. 10, 2020, pp. 1638–1647.
19.
CecenR. K.Multi-Objective TMA Management Optimization Using the Point Merge System. Aircraft Engineering and Aerospace Technology, Vol. 93, No. 1, 2021, pp. 15–24.
20.
CecenR. K.ÇetekF. A.Optimising Aircraft Arrivals in Terminal Airspace by Mixed Integer Linear Programming Model. The Aeronautical Journal, Vol. 124, No. 1278, 2020, pp. 1129–1145.
21.
BalakrishnanH.ChandranB. G.Algorithms for Scheduling Runway Operations Under Constrained Position Shifting. Operations Research, Vol. 58, No. 6, 2010, pp. 1650–1665.
22.
BoysenN.FliednerM.Scheduling Aircraft Landings to Balance Workload of Ground Staff. Computers & Industrial Engineering, Vol. 60, No. 2, 2011, pp. 206–217.
23.
LiederA.BriskornD.StolletzR.A Dynamic Programming Approach for the Aircraft Landing Problem with Aircraft Classes. European Journal of Operational Research, Vol. 243, No. 1, 2015, pp. 61–69.
24.
BennellJ. A.MesgarpourM.PottsC. N.Dynamic Scheduling of Aircraft Landings. European Journal of Operational Research, Vol. 258, No. 1, 2017, pp. 315–327.
25.
MontoyaJ.RathinamS.WoodZ.Multiobjective Departure Runway Scheduling Using Dynamic Programming. IEEE Transactions on Intelligent Transportation Systems, Vol. 15, No. 1, 2013, pp. 399–413.
26.
RathinamS.WoodZ.SridharB.JungY.A Generalized Dynamic Programming Approach for a Departure Scheduling Problem. In AIAA Guidance, Navigation, and Control Conference, Chicago, IL, 2009.
27.
D’ArianoA.PistelliM.PacciarelliD.Aircraft Retiming and Rerouting in Vicinity of Airports. IET Intelligent Transport Systems, Vol. 6, No. 4, 2012, pp. 433–443.
28.
SölvelingG.ClarkeJ. P.Scheduling of Airport Runway Operations Using Stochastic Branch and Bound Methods. Transportation Research Part C: Emerging Technologies, Vol. 45, 2014, pp. 119–137.
29.
SamàM.D’ArianoA.PacciarelliD.Rolling Horizon Approach for Aircraft Scheduling in the Terminal Control Area of Busy Airports. Procedia-Social and Behavioral Sciences, Vol. 80, 2013, pp. 531–552.
30.
AtkinJ. A.BurkeE. K.GreenwoodJ. S.ReesonD.Hybrid Metaheuristics to Aid Runway Scheduling at London Heathrow Airport. Transportation Science, Vol. 41, No. 1, 2007, pp. 90–106.
31.
FuriniF.KiddM. P.PersianiC. A.TothP.Improved Rolling Horizon Approaches to the Aircraft Sequencing Problem. Journal of Scheduling, Vol. 18, No. 5, 2015, pp. 435–447.
32.
HancerliogullariG.RabadiG.Al-SalemA. H.KharbecheM.Greedy Algorithms and Metaheuristics for a Multiple Runway Combined Arrival Departure Aircraft Sequencing Problem. Journal of Air Transport Management, Vol. 32, 2013, pp. 39–48.
33.
SalehipourA.Moslemi NaeniL.KazemipoorH.Scheduling Aircraft Landings by Applying a Variable Neighborhood Descent Algorithm: Runway Dependent Landing Time Case. Journal of Applied Operational Research, Vol. 1, No. 1, 2009, pp. 39–49.
34.
LiangM.DelahayeD.MarechalP.Conflict-Free Arrival and Departure Trajectory Planning for Parallel Runway with Advanced Point-Merge System. Transportation Research Part C: Emerging Technologies, Vol. 95, 2018, pp. 207–227.
35.
CecenR.CetekC.KayaO.Aircraft Sequencing and Scheduling in TMAs Under Wind Direction Uncertainties. The Aeronautical Journal, Vol. 124, No. 1282, 2020, pp. 1896–1912.
36.
JiangY.XuZ.XuX.LiaoZ.LuoY.A Schedule Optimization Model on Multirunway Based on Ant Colony Algorithm. Mathematical Problems in Engineering, Vol. 2014, 2014, p. 368208.
37.
ZhanZ. H.ZhangJ.LiY.LiuO.KwokS. K.IpW. H.KaynakO.An Efficient Ant Colony System Based on Receding Horizon Control for the Aircraft Arrival Sequencing and Scheduling Problem. IEEE Transactions on Intelligent Transportation Systems, Vol. 11, No. 2, 2010, pp. 399–412.
38.
XuB.An Efficient Ant Colony Algorithm Based on Wake-Vortex Modeling Method for Aircraft Scheduling Problem. Journal of Computational and Applied Mathematics, Vol. 317, 2017, pp. 157–170.
39.
BeasleyJ.E.SonanderJ.HavelockP.Scheduling Aircraft Landings at London Heathrow Using a Population Heuristic. Journal of the Operational Research Society, Vol. 52, No. 5, 2001, pp. 483–493.
40.
HuX. B.ChenW. H.Receding Horizon Control for Aircraft Arrival Sequencing and Scheduling. IEEE Transactions on Intelligent Transportation Systems, Vol. 6, No. 2, 2005, pp. 189–197.
41.
HuX. B.PaoloE. D.An Efficient Genetic Algorithm with Uniform Crossover for Air Traffic Control. Computers & Operations Research, Vol. 36, No. 1, 2009, pp. 245–259.
42.
HuX. B.Di PaoloE. A.A Ripple-Spreading Genetic Algorithm for the Aircraft Sequencing Problem. Evolutionary Computation, Vol. 19, No. 1, 2011, pp. 77–106.
43.
MokhtarimousaviS.RahamiH.KavehA.Multi-Objective Mathematical Modelling of Aircraft Landing Problem on a Runway in Static Mode, Scheduling and Sequence Determination Using NSGA-II. Iran University of Science & Technology, Vol. 5, No. 1, 2015, pp. 21–36.
44.
MaY.HuM.ZhangH.YinJ.WuF.Optimized Method for Collaborative Arrival Sequencing and Scheduling in Metroplex Terminal Area. Acta Aeronautica et Astronautica Sinica, Vol. 36, No. 7, 2015, pp. 2279–2290.
45.
MaY.HuM.YinJ.TianW.Collaborative Sequencing and Scheduling Method for Arrival and Departure Traffic Flow in Multi-Airport Terminal Area. Acta Aeronautica et Astronautica Sinica, Vol. 38, No. 2, 2017, p. 320222.
46.
YinJ.MaY.HuY.HanK.YinS.XieH.Delay, Throughput and Emission Tradeoffs in Airport Runway Scheduling with Uncertainty Considerations. Networks and Spatial Economics, Vol. 21, 2021, pp. 85–122.
47.
PinolH.BeasleyJ. E.Scatter Search and Bionomic Algorithms for the Aircraft Landing Problem. European Journal of Operational Research, Vol. 171, No. 2, 2006, pp. 439–462.
48.
HongY.ChoN.KimY.ChoiB.Multiobjective Optimization for Aircraft Arrival Sequencing and Scheduling. Journal of Air Transportation, Vol. 25, No. 4, 2017, pp. 115–122.
49.
GirishB. S.An Efficient Hybrid Particle Swarm Optimization Algorithm in a Rolling Horizon Framework for the Aircraft Landing Problem. Applied Soft Computing, Vol. 44, 2016, pp. 200–221.
50.
NgK. K. HLeeC. K. M.ChanF. T.QinY.Robust Aircraft Sequencing and Scheduling Problem with Arrival/Departure Delay Using the Min-Max Regret Approach. Transportation Research Part E: Logistics and Transportation Review, Vol. 106, 2017, pp. 115–136.
51.
KaplanZ.ÇetekC.Yapay bağışıklık metasezgiseli ile tek pistli havaalanlarında iniş sıralamasının eniyilemesi. Eskişehir Osmangazi Üniversitesi Mühendislik ve Mimarlık Fakültesi Dergisi, Vol. 28, No. 3, 2020, pp. 321–331.
52.
ArtiouchineK.BaptisteP.DürrC.Runway Sequencing with Holding Patterns. European Journal of Operational Research, Vol. 189, No. 3, 2008, pp. 1254–1266.
53.
DelgadoL.PratsX.En Route Speed Reduction Concept for Absorbing Air Traffic Flow Management Delays. Journal of Aircraft, Vol. 49, No. 1, 2012, pp. 214–224.
54.
Air Traffic Management. International Civil Aviation Organization Rules of the Air and Air Traffic Services Doc 4444 ATM 501. Fifteenth Edition. ICAO Publications, Montreal, Canada, 2007.
55.
BADA. User Manual for the Base of Aircraft Data (BADA) Revision 3.11. BADA, Brétigny-sur-Orge, Fransa, 2013.
56.
OmerJ.A Space-Discretized Mixed-Integer Linear Model for Air-Conflict Resolution with Speed and Heading Maneuvers. Computers & Operations Research, Vol. 58, 2015, pp. 75–86.
57.
RobersonB.Fuel Conservation Strategies: Cost Index Explained. Boeing Aero Quarterly, Vol. 2, 2007, pp. 26–28.
