Planning efficient 4D trajectories in Air Traffic Flow Management

Abstract In this paper, we focus on designing efficient 4D trajectories for the planning phase of Air Traffic Flow Management (ATFM). A key feature of the proposed approach is the inclusion of stakeholders’ preferences and priorities. In particular, we have implemented two priority mechanisms recently developed by Eurocontrol, namely the Fleet Delay Reordering and the Margins. For this purpose, we have customized a multi-objective binary program for the ATFM problem taking into account the specific assumptions required for the ATFM planning phase. To compute the Pareto frontier in a reasonable computational time, we have developed a simulated annealing algorithm. The algorithm has been tested on an instance resembling real world conditions using data extracted from the Eurocontrol data repository. This instance involves four major European airports and their air traffic in one of the busiest days of year 2016, and precisely, October 3rd. The simulated annealing algorithm has shown good computational performances and has provided a good approximation of the Pareto optimal frontier. The results have been validated using Eurocontrol tools and have demonstrated the viability of the proposed approach. Practitioners and stakeholders’ representatives have provided positive feedback on the proposed modeling approach and on the inclusion of ATM stakeholders’ preferences and priorities.

[1]  Antonio J. Nebro,et al.  jMetal: A Java framework for multi-objective optimization , 2011, Adv. Eng. Softw..

[2]  Lin Xie,et al.  Simulated annealing approach to nurse rostering benchmark and real-world instances , 2019, Ann. Oper. Res..

[3]  D.A. Van Veldhuizen,et al.  On measuring multiobjective evolutionary algorithm performance , 2000, Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512).

[4]  Hisao Ishibuchi,et al.  Modified Distance Calculation in Generational Distance and Inverted Generational Distance , 2015, EMO.

[5]  Amedeo R. Odoni,et al.  The European Air Traffic Flow Management Problem , 2007, Transp. Sci..

[6]  Konstantinos G. Zografos,et al.  An optimization model for assigning 4D-trajectories to flights under the TBO concept , 2017 .

[7]  Kathryn A. Dowsland,et al.  Simulated Annealing , 1989, Encyclopedia of GIS.

[8]  Martin W. P. Savelsbergh,et al.  The Quadrant Shrinking Method: A simple and efficient algorithm for solving tri-objective integer programs , 2017, Eur. J. Oper. Res..

[9]  Laureano F. Escudero,et al.  On air traffic flow management with rerouting. Part I: Deterministic case , 2012, Eur. J. Oper. Res..

[10]  Mark Fleischer,et al.  The measure of pareto optima: Applications to multi-objective metaheuristics , 2003 .

[11]  Xiaojing Li,et al.  A simulated annealing for multi-criteria network path problems , 2012, Comput. Oper. Res..

[12]  Amedeo R. Odoni,et al.  An Integer Optimization Approach to Large-Scale Air Traffic Flow Management , 2011, Oper. Res..

[13]  S. Torres,et al.  An integrated approach to air traffic management to achieve trajectory based operations , 2012, 2012 IEEE/AIAA 31st Digital Avionics Systems Conference (DASC).

[14]  Konstantinos G. Zografos,et al.  Incorporating Stakeholders’ priorities and preferences in 4D trajectory optimization , 2018 .

[15]  Avijit Mukherjee,et al.  Air Traffic Flow Management , 2012 .

[16]  Anh Tuan Nguyen,et al.  A performance comparison of multi-objective optimization algorithms for solving nearly-zero-energy-building design problems , 2016 .

[17]  Hanif D. Sherali,et al.  An Airspace Planning Model for Selecting Flight-plans Under Workload, Safety, and Equity Considerations , 2002, Transp. Sci..

[18]  Sophie Constans,et al.  Computing 4D near-optimal trajectories for dynamic air traffic flow management with column generation and branch-and-price , 2011 .

[19]  E. L. Ulungu,et al.  MOSA method: a tool for solving multiobjective combinatorial optimization problems , 1999 .