Solving the Air Conflict Resolution Problem Under Uncertainty Using an Iterative Biobjective Mixed Integer Programming Approach

Published Online:https://doi.org/10.1287/trsc.2016.0714

References

  • Alliot JM, Durand N (2011) A mathematical analysis of the influence of wind uncertainty on MTCD efficiency. The Controller.Google Scholar
  • Alonso-Ayuso AA, Escudero LF, Martín-Campo FJ (2012) A mixed 0-1 nonlinear optimization model and algorithmic approach for the collision avoidance in ATM: Velocity changes through a time horizon. IEEE Trans. Intelligent Transportation Systems 39(12):3136–3146.Google Scholar
  • Alonso-Ayuso A, Escudero LF, Martín-Campo FJ (2016) Multiobjective optimization for aircraft conflict resolution. A metaheuristic approach. Eur. J. Oper. Res. 248(2):691–702.CrossrefGoogle Scholar
  • Archibald JK, Hill JC, Jepsen N, Stirling WC, Frost RL (2008) A satisficing approach to aircraft conflict resolution. IEEE Trans. Systems, Man, Cybernetics, Part C: Appl. Rev. 38(4):510–521.CrossrefGoogle Scholar
  • Ballin MG, Erzberger H (1996) An analysis of landing rates and separations at the Dallas/Fort Worth International Airport. Technical report, Ames Research Center, Moffett Field, CA.Google Scholar
  • Chaloulos G, Lygeros J (2007) Effect of wind correlation on aircraft conflict probability. J. Guidance, Control, Dynam. 30(6):1742–1752.CrossrefGoogle Scholar
  • Cole RE, Richard C, Kim S, Bailey D (1998) Assessment of the Rapid Update Cycle (RUC) with near real-time aircraft reports. Technical report, Lincoln Laboratory, Massachusetts Institute of Technology, Cambridge.Google Scholar
  • Durand N, Alliot JM, Noailles J (1996) Automatic aircraft conflict resolution using genetic algorithms. Proc. ACM Sympos. Appl. Comput. (ACM, New York), 289–298.CrossrefGoogle Scholar
  • Erzberger H, Paielli RA, Isaacson DR, Eshow MM (1997) Conflict detection and resolution in the presence of prediction error. 1st USA/Europe Air Traffic Management R&D Seminar, Saclay, France, 17–20.Google Scholar
  • EUROCONTROL (2011) User manual for the Base of Aircraft Data (BADA), Revision 3.9. Technical report, Eurocontrol, Brussels.Google Scholar
  • EUROCONTROL (2013) Eurocontrol long-term forecast: IFR flight movements 2013–2035. Technical report, Eurocontrol, Brussels.Google Scholar
  • Federal Aviation Administration (2011) Introduction to TCAS II, Version 7.1. Technical report, Federal Aviation Administration, U.S. Department of Transportation, Washington, DC.Google Scholar
  • Fortet R (1960) Applications de lálgèbre de Boole en recherche opérationelle. Revue Française Recherche Opérationelle 4(14):17–26.Google Scholar
  • Haddad R, Carlier J, Moukrim A (2008) A new combinatorial approach for coordinating aerial conflicts given uncertainties regarding aircraft speeds. Internat. J. Production Econom. 112(1):226–235.CrossrefGoogle Scholar
  • IBM (2014) IBM ILOG CPLEX v12.5. User’s manual for CPLEX. Technical report 11/03/08-08, IBM.Google Scholar
  • Irvine R (2002) A geometrical approach to conflict probability estimation. Air Traffic Control Quart. 10(2):85–113.CrossrefGoogle Scholar
  • Joint Planning and Development Office (2008) NextGen air transportation system integrated work plan. Technical report, Joint Planning and Development Office, Washington, DC.Google Scholar
  • Lehouillier T, Omer J, Soumis F, Desaulniers G (2017) Two decomposition algorithms for solving a minimum weight maximum clique model for the air conflict resolution problem. Eur. J. Oper. Res. 256(3):696–712.CrossrefGoogle Scholar
  • Lehouillier T, Soumis F, Omer J, Allignol C (2016) Measuring the interactions between air traffic control and flow management using a simulation-based framework. Comput. Indust. Engrg. 99:269–279.CrossrefGoogle Scholar
  • Lygeros J, Prandini M (2002) Aircraft and weather models for probabilistic collision avoidance in air traffic control. Proc. 41st IEEE Conf. Decision Control, Vol. 3, 2427–2432.CrossrefGoogle Scholar
  • Lymperopoulos I (2010) Sequential Monte Carlo methods in air traffic management. Unpublished doctoral dissertation, ETH Zürich.Google Scholar
  • Martín-Campo FJ (2010) The collision avoidance problem: Methods and algorithms. Unpublished doctoral thesis, Rey Juan Carlos University, Madrid.Google Scholar
  • Menon PK, Sweriduk GD, Sridhar B (1999) Optimal strategies for free-flight air traffic conflict resolution. J. Guidance, Control, Dynam. 22(2):202–211.CrossrefGoogle Scholar
  • Omer J (2015) A space-discretized mixed-integer linear model for air-conflict resolution with speed and heading maneuvers. Comput. Oper. Res. 58:75–86.CrossrefGoogle Scholar
  • Omer J, Farges JL (2013) Hybridization of nonlinear and mixed-integer linear programming for aircraft separation with trajectory recovery. IEEE Trans. Intelligent Transportation Systems 14(3):1218–1230.CrossrefGoogle Scholar
  • Paielli RA (2003) Modeling maneuver dynamics in air traffic conflict resolution. J. Guidance, Control, Dynam. 26(3):407–415.CrossrefGoogle Scholar
  • Pallottino L, Feron EM, Bicchi A (2002) Conflict resolution problems for air traffic management systems solved with mixed integer programming. IEEE Trans. Intelligent Transportation Systems 3(1):3–11.CrossrefGoogle Scholar
  • Prandini M, Hu J, Lygeros J, Sastry S (2000) A probabilistic approach to aircraft conflict detection. IEEE Trans. Intelligent Transportation Systems 1(4):199–220.CrossrefGoogle Scholar
  • Rey D, Rapine C, Fondacci R, El Faouzi NE (2016) Subliminal speed control in air traffic management: Optimization and simulation. Transportation Sci. 50(1):240–262.LinkGoogle Scholar
  • Rubinstein RY, Kroese DP (2011) Simulation and the Monte Carlo Method, Vol. 707 (John Wiley & Sons, Chichester, UK).Google Scholar
  • Schouwenaars T (2006) Safe trajectory planning of autonomous vehicles. Unpublished doctoral thesis, Massachusetts Institute of Technology, Cambridge.Google Scholar
  • Schwartz BE, Benjamin SG, Green SM, Jardin MR (2000) Accuracy of RUC-1 and RUC-2 wind and aircraft trajectory forecasts by comparison with ACARS observations. Weather Forecasting 15(3):313–326.CrossrefGoogle Scholar
  • SESAR Joint Undertaking (2012) European ATM master plan, 2nd ed. Technical report, SESAR Joint Undertaking, Brussels.Google Scholar
  • Stirling WC, Goodrich MA (1999) Satisficing games. Inform. Sci. 114(1):255–280.CrossrefGoogle Scholar
  • Tomlin C, Pappas GJ, Sastry S (1998) Conflict resolution for air traffic management: A study in multiagent hybrid systems. IEEE Trans. Automatic Control 43(4):509–521.CrossrefGoogle Scholar
  • Vela AE, Salaun E, Solak S, Feron E, Singhose W, Clarke JP (2009) A two-stage stochastic optimization model for air traffic conflict resolution under wind uncertainty. IEEE/AIAA 28th Digital Avionics Systems Conf. (Endocrine Society, Washington, DC), 2.E.5–1–2.E.5–13.CrossrefGoogle Scholar
  • Visintini AL, Glover W, Lygeros J, Maciejowski J (2006) Monte Carlo optimization for conflict resolution in air traffic control. IEEE Trans. Intelligent Transportation Systems 7(4):470–482.CrossrefGoogle Scholar
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