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Impact of Collaborative Decision Making in Optimized Air Traffic Control: A Game Theoretical Approach

  • Manish Tripathy
  • Marcella Samà
  • Francesco CormanEmail author
  • Gabriel Lodewijks
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9855)

Abstract

Air traffic is growing, putting increasing stress to airports and air traffic control. The introduction of optimized approaches, based on mathematical optimization paradigms for planning and real time control, can be a possible solution to this issues. We investigate the practical setting of an advanced optimization algorithm in a real-life setting of a major airport where traffic is diverse, belonging to multiple companies. We compare to the incumbent practice (based on First Come First Served) in order to determine a gap with optimized solutions computed by advanced algorithms. Those are based on a job shop scheduling model and solved by a commercial solver.

This paper analyses the benefit for the involved operators of such approaches by associating a monetary cost/benefit to operations. Cooperative game theory tools have been used in the analysis. In particular, we use the Shapley value to determine the fair distribution of the costs based on the marginal improvement that the optimization of the traffic belonging to any airline brought to the system. The main conclusions of this study are the determination of the superior performance in terms of minimising the delay experienced by the whole airport, which reaches more than 25 %. The benefit allocation gives share of benefits more insightful than a simple proportional approaches based on share of traffic, or share of delay. The practical implications of the analysis with regard to variety in benefits as well as possible implementations by the different operators and companies are also analysed.

Keywords

Air traffic control Collaborative decision making Game theory Aircraft scheduling problem 

References

  1. Ball, M.O., Barnhart, C., Nemhauser, G., Odoni, A.: Air transportation: irregular operations and control. In: Handbooks in Operations Research and Management Science, vol. 14, no. 1, pp. 1–68 (2007)Google Scholar
  2. Ball, M.O., Barnhart, C., Dresner, M., Hansen, M., Neels, K.: Total Delay Impact Study. NEXTOR (2010a) Google Scholar
  3. Ball, M.O., Hoffman, R., Mukherjee, A.: Ground delay program planning under uncertainty based on the ration-by-distance principle. Transp. Sci. 44(1), 1–14 (2010b) Google Scholar
  4. Bäuerle, N., Engelhardt-Funke, O., Kolonko, M.: On the waiting time of arriving aircraft and the capacity of airports with one or two runways. Eur. J. Oper. Res. 177(2), 1180–1196 (2007)CrossRefzbMATHGoogle Scholar
  5. Beasley, J., Krishnamoorty, M., Sharaiha, Y., Abramson, D.: Scheduling aircraft landing - the static case. Transp. Sci. 34(2), 180–197 (2000)CrossRefzbMATHGoogle Scholar
  6. Bennell, J.A., Mesgarpour, M., Potts, C.N.: Airport runway scheduling. 4OR – Q. J. Oper. Res. 4(2), 115–138 (2011)CrossRefzbMATHMathSciNetGoogle Scholar
  7. Bertsimas, D., Gupta, S.: Fairness in air traffic flow management. In: INFORMS Meeting, CA, USA (2009)Google Scholar
  8. Bertsimas, D., Gupta, S.: Fairness and collaboration in network air traffic flow management: an optimization approach. Transp. Sci. 50(1), 57–76 (2015)CrossRefGoogle Scholar
  9. Bertsimas, D., Farias, V., Trichakis, N.: The price of fairness. Oper. Res. 59(1), 17–31 (2011)CrossRefzbMATHMathSciNetGoogle Scholar
  10. Carr, G., Erzberger, H., Neuman, F.: Airline arrival prioritization in sequencing and scheduling. In: 2nd USA/EUROPE Air Traffic Management R&D Seminar, pp. 1–11 (1998)Google Scholar
  11. CDM/FAA: Improving Air Traffic Management Together (2015). http://cdm.fly.faa.gov/
  12. Clayton, E., Hilz, A.: 2015 Aviation Trends - Efficiency & Attitudes, Strategy& (2015). http://www.strategyand.pwc.com/perspectives/2015-aviation-trends
  13. Corman, F., D’Ariano, A., Hansen, I.A., Pacciarelli, D.: Optimal multi-class rescheduling of railway traffic. J. Rail Trans. Plann. Manag. 1(1), 14–24 (2011)CrossRefGoogle Scholar
  14. Corman, F., D’ariano, A., Pacciarelli, D., Pranzo, M.: Dispatching and coordination in multi-area railway traffic management. Comput. Oper. Res. 44, 146–160 (2015)CrossRefzbMATHGoogle Scholar
  15. D’Ariano, A., Pacciarelli, D., Pistelli, M., Pranzo, M.: Real-time scheduling of aircraft arrivals and departures in a terminal maneuvering area. Networks 65(3), 212–227 (2015)CrossRefMathSciNetGoogle Scholar
  16. FAA. Federal Aviation Administration (n.d.). https://www.faa.gov/air_traffic/
  17. Gröflin, H., Klinkert, A.: Scheduling with generalized disjunctive graphs: feasibility issues. In: XV Conference on European Chapter on Combinatorial Optimization (2002) Google Scholar
  18. IATA: Annual review (2015)Google Scholar
  19. Lempert, R.: RAND - Infrastructure, Safety and Environment. RAND Corporation (2012)Google Scholar
  20. Littlechild, S., Owen, G.: A simple expression for the Shapley value in a special case. Manag. Sci. 20(3), 370–372 (1973)CrossRefzbMATHGoogle Scholar
  21. Luenberger, R.A.: A traveling-salesman-based approach to aircraft scheduling in the terminal area. NASA Technical report 100062 (1988)Google Scholar
  22. Mascis, A., Pacciarelli, D.: Job shop scheduling with blocking and no-wait constraints. Eur. J. Oper. Res. 143(3), 498–517 (2002)CrossRefzbMATHMathSciNetGoogle Scholar
  23. Mason, S.J., Oey, K.: Scheduling complex job shops using disjunctive graphs: a cycle elimination procedure. Int. J. Prod. Res. 41(5), 981–994 (2003)CrossRefGoogle Scholar
  24. Roy, S., Sussman, B.: Les Problèmes d’ordonnancement avec contraintes disjonctives. Note DS n.9 bis, SEMA, Montrouge (1964)Google Scholar
  25. Samà, M., D’Ariano, A., D’Ariano, P., Pacciarelli, D.: Optimal aircraft scheduling and routing at a terminal control area during disturbances. Transp. Res. Part C 47(1), 61–85 (2014)CrossRefGoogle Scholar
  26. Samà, M.A., D’Ariano, A., D’Ariano, P., Pacciarelli, D.: Scheduling models for optimal aircraft traffic control at busy airports: tardiness, priorities, equity and violations considerations. Omega (2016). doi: 10.1016/j.omega.2016.04.003 Google Scholar
  27. Samà, M., D’Ariano, A., Pacciarelli, D.: Rolling horizon approach for aircraft scheduling in the terminal control area of busy airports. Transp. Res. Part E 60(1), 140–155 (2013)CrossRefGoogle Scholar
  28. Shapley, L.S.: A value for n-person games. Ann. Math. Stud. 28, 307–317 (1953)zbMATHMathSciNetGoogle Scholar
  29. Skowron, P., Rzadca, K.: Fair share is not enough: measuring fairness in scheduling with cooperative game theory. In: Wyrzykowski, R., Dongarra, J., Karczewski, K., Waśniewski, J. (eds.) PPAM 2013, Part II. LNCS, vol. 8385, pp. 38–48. Springer, Heidelberg (2014)CrossRefGoogle Scholar
  30. Soomer, M.J., Franx, G.J.: Scheduling aircraft landings using airlines’ preferences. Eur. J. Oper. Res. 190(1), 277–291 (2008)CrossRefzbMATHMathSciNetGoogle Scholar
  31. Von Neumann, J., Morgenstern, O.: Theory of Games and Economic Behavior. Princeton University Press, Princeton (1953)zbMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Manish Tripathy
    • 1
    • 2
  • Marcella Samà
    • 3
  • Francesco Corman
    • 1
    Email author
  • Gabriel Lodewijks
    • 1
  1. 1.Transport Engineering and LogisticsDelft University of TechnologyDelftThe Netherlands
  2. 2.Fuqua School of BusinessDuke UniversityDurhamUSA
  3. 3.Department of EngineeringRoma Tre UniversityRomeItaly

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