Abstract
This paper addresses the real-time aircraft routing and scheduling problem at a busy terminal control area (TCA) in case of traffic congestion. The problem of effectively managing TCA operations is particularly challenging, since there is a continuous growth of traffic demand and the TCAs are becoming the bottleneck of the entire air traffic control system. The resulting increase in airport congestion, economic and environmental penalties can be measured in terms of several performance indicators, including take-off and landing aircraft delays and energy consumption. This work addresses this problem via the development of mixed-integer linear programming formulations that incorporate the safety rules with high modeling precision and objective functions of practical interest based on the minimization of the total travel time and the largest delay due to potential aircraft conflicts. Computational experiments are performed on real-world data from Roma Fiumicino, the largest airport in Italy in terms of passenger demand. Traffic disturbances are generated by simulating sets of random landing/take-off aircraft delays. Near-optimal solutions of practical-size instances are computed in a short time via a commercial solver. The computational analysis enables the selection of those solutions offering the best compromise among the different objectives.
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Abdelghany KF, Abdelghany AF, Ekollu G (2008) An integrated decision support tool for airlines schedule recovery during irregular operations. Eur J Oper Res 185(2):825–848
Agnetis A, De Pascale G, Pranzo M (2009) Computing the Nash solution for scheduling bargaining problems. Int J Oper Res 6(1):54–69
Andreatta G, Capanna L, De Giovanni L, Monaci M, Righi L (2014) Efficiency and robustness in integrated airport apron, a support platform for intelligent airport ground handling. J Intell Transp Syst 18(1):121–130
Atkin JAD, Burke EK, Greenwood JS, Reeson D (2009) An examination of take-off scheduling constraints at London Heathrow airport. Public Transp Plan Oper 1:169–187
Atkin JAD, Burke EK, Greenwood JS (2010) TSAT allocation at London Heathrow: the relationship between slot compliance, throughput and equity. Public Transp Plan Oper 2:173–198
Ball M, Barnhart C, Nemhauser G, Odoni A (2007) Air transportation: irregular operations and control. Handb Oper Res Manag Sci 14(1):1–67
Barnhart C, Fearing D, Odoni A, Vaze V (2012) Demand and capacity management in air transportation. EURO J Transp Logist 1(1–2):135–155
Bennell JA, Mesgarpour M, Potts CN (2011) Airport runway scheduling. 4OR Q J Oper Res 4(2):115–138
Bertsimas D, Frankovich M, Odoni A (2011) Optimal selection of airport runway configurations. Oper Res 59(6):1407–1419
Bertsimas D, Lulli G, Odoni A (2011) An integer optimization approach to large-scale air traffic flow management. Oper Res 59(1):211–227
Bianco L, Dell’Olmo P, Giordani S (2006) Scheduling models for air traffic control in terminal areas. J Sched 9(3):180–197
Bubalo B, Daduna JR (2011) Airport capacity and demand calculations by simulation—the case of Berlin-Brandenburg International Airport. NETNOMICS 12:161–181
Castelli L, Pesenti R, Ranieri A (2011) The design of a market mechanism to allocate Air Traffic Flow Management slots. Transp Res Part C 19(5):931–943
Churchill AM, Lovell DJ, Ball MO (2010) Flight delay propagation impact on strategic air traffic flow management. Transp Res Records 2177:105–113
Clausen J, Larsen A, Larsen J, Rezanova NJ (2010) Disruption management in the airline industry—concepts, models and methods. Comput Oper Res 37(5):809–821
D’Ariano A, Pacciarelli D, Pranzo M (2007) A branch and bound algorithm for scheduling trains in a railway network. Eur J Oper Res 183(2):643–657
D’Ariano A, D’Urgolo P, Pacciarelli D, Pranzo M (2010) Optimal sequencing of aircrafts take-off and landing at a busy airport. In: Proceedings of the 13th international IEEE annual conference on intelligent transportation systems, Madeira Island, pp 1569–1574
D’Ariano A, Pistelli M, Pacciarelli D (2012) Aircraft retiming and rerouting in vicinity of airports. IET Intell Transp Syst J 6(4):433–443
D’Ariano A, Pacciarelli D, Pistelli M, Pranzo M (2015) Real-time scheduling of aircraft arrivals and departures in a terminal maneuvering area. Networks. Wiley, New York. doi:10.1002/net.21599
Djokic J, Lorenz B, Fricke H (2010) Air traffic control complexity as workload driver. Transp Res Part C 18(6):930–936
Kalai E, Smorodinsky M (1975) Other solutions to Nash’s bargaining problem. Econometrica 43(3):513–518
Kim J, Kröller A, Mitchell JSB, Sabhnani GR (2009) Scheduling aircraft to reduce controller workload. In: Clausen J, Di Stefano G (eds) Proceedings of the 9th workshop on algorithmic approaches for transportation modeling, optimization, and systems, Copenhagen
Kohl N, Larsen A, Larsen J, Ross A, Tiourine S (2007) Airline disruption management—perspectives, experiences and outlook. J Air Transp Manag 13(3):149–162
Kuchar JK, Yang LC (2000) A review of conflict detection and resolution modeling methods. IEEE Trans Intell Transp Syst 4(1):179–189
Mascis A, Pacciarelli D (2002) Job shop scheduling with blocking and no-wait constraints. Eur J Oper Res 143(3):498–517
Palagachev KD, Rieck M, Gerdts M (2013) A mixed-integer optimal control approach for aircraft landing model. In: Proceedings of the 3rd international conference on models and technologies for intelligent transport systems, Dresden
Pellegrini P, Castelli L, Pesenti R (2012) Metaheuristic algorithms for the simultaneous slot allocation problem. IET Intell Transp Syst 6(4):453–462
Pellegrini P, Rodriguez J (2013) Single European sky and single European railway area: a system level analysis of air and rail transportation. Transp Res Part A 57(1):64–86
Prevot T, Homola J, Martin H, Mercer J (2011) Automated air traffic control operations with weather and time-constraints. Ninth USA/Europe air traffic management research and development seminar, Berlin
Ravizza S, Chen J, Atkin JAD, Burke EK, Stewart P (2013) The trade-off between taxi time and fuel consumption in airport ground movement. Public Transp Plan Oper 5:25–40
Samà M, D’Ariano A, Pacciarelli D (2013a) Rolling horizon approach for aircraft scheduling in the terminal control area of busy airports. Transp Res Part E 60(1):140–155
Samà M, D’Ariano A, D’Ariano P, Pacciarelli D (2013b) Scheduling models for optimal aircraft traffic control at busy airports: tardiness, priorities, equity and violations considerations. In: Tech. Rep. RT-DIA-205-2013, Dipartimento di Ingegneria, Roma Tre University
Witt A, Voss S (2010) Job shop scheduling with buffer constraints and jobs consuming variable buffer space. Lecture Notes in Business Information Processing, vol 46. In: Dangelmaier W, Blecken A, Delius R, Klöpfer S (eds) Advanced manufacturing and sustainable logistics. Springer, Berlin, pp 295–307
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Appendix
Appendix
Figure 3 presents a numerical example of traffic flows for FCO TCA. We consider two landing aircraft (named A and B) and a taking-off aircraft (named C). The following routes are considered by the solver. Aircraft A has two routes: 3-6-8-10-11-13 is the default route, and 3-6-8-11-12 is the alternative route. Aircraft B has the default route 1-4-7-10-11-13, while aircraft C has the default route 12.
The entrance (exit) due date of A is 40 (640), the entrance (exit) due date of B is 0 (640), the entrance (exit) due date of C is 630 (630). The release time of each aircraft is equal to the corresponding entrance due date time. The travel time of each aircraft in each resource is reported in Fig. 4.
Figure 4 a (b) gives the Gantt diagram of the optimal ATFM-TCA solution for the default (alternative) routes. In the case with default routes, the routes of aircraft A and B are conflicting and the optimal sequencing order is first B and then A. The consecutive delay of A is 46 at the entrance (a too small delay to consider half circles in the holding resource) and 95 at the runway. In the case with alternative routes, the routes of aircraft A and C are conflicting and the optimal sequencing order is first A and then C. Thus, the consecutive delay of C is 20.
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Samà, M., D’Ariano, A., D’Ariano, P. et al. Air traffic optimization models for aircraft delay and travel time minimization in terminal control areas. Public Transp 7, 321–337 (2015). https://doi.org/10.1007/s12469-015-0103-x
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DOI: https://doi.org/10.1007/s12469-015-0103-x