A Two-Stage Airport Ground Movement Speed Profile Design Methodology Using Particle Swarm Optimization
Automation of airport operations can greatly improve
ground movement efficiency. In this paper, we study the speed profile
design problem for advanced airport ground movement control and
guidance. The problem is constrained by the surface four-dimensional
trajectory generated in taxi planning. A decomposed approach of two
stages is presented to solve this problem efficiently. In the first stage,
speeds are allocated at control points, which ensure smooth speed
profiles can be found later. In the second stage, detailed speed profiles
of each taxi interval are generated according to the allocated control
point speeds with the objective of minimizing the overall fuel
consumption. We present a swarm intelligence based algorithm for the
first-stage problem and a discrete variable driven enumeration method
for the second-stage problem, since it only has a small set of discrete
variables. Experimental results demonstrate the presented
methodology performs well on real world speed profile design
problems.
[1] SESAR Definition Phase 1: Deliverable 1, Eurocontrol, 2006.
[2] Advanced Surface Movement Guidance and Control Systems (A-SMGCS)
Manual, International Civil Aviation Organization, 2004.
[3] V. H. L. Cheng, “Research Progress on an Automation Concept for
Surface Operation with Time-Based Trajectories,” in Proc. of the
Integrated Communications, Navigation and Surveillance Conference,
2007, pp. 1 – 13.
[4] Á. G. Marín, “Airport Management: Taxi Planning,” Annals of
Operations Research, vol. 143, no. 1, pp. 191-202, 2006.
[5] J. A. Atkin, E. K. Burke, and S. Ravizza, “The Airport Ground Movement
Problem: Past and Current Research and Future Directions,” in Proc. of
the 4th International Conference on Research in Air Transportation
(ICRAT), 2010, pp. 131-138.
[6] J. W. Smeltink and M. J. Soomer, “An Optimisation Model for Airport
Taxi Scheduling,” in Proc. of INFORMS Annual Meeting, 2004.
[7] P. C. Roling and H. G. Visser, “Optimal Airport Surface Traffic Planning
Using Mixed-Integer Linear Programming,” International Journal of
Aerospace Engineering, vol. 2008, pp. 1-11, 2008.
[8] G. L. Clare and A. G. Richards, “Optimization of Taxiway Routing and
Runway Scheduling,” IEEE Trans. Intelligent Transportation Systems,
vol. 12, no. 4, pp. 1000-1013, 2011.
[9] Á. G. Marín, “Airport Taxi Planning: Lagrangian Decomposition,”
Journal of Advanced Transportation, vol. 47, no. 4, pp. 461-474, 2011.
[10] V. H. L. Cheng and G. D. Sweriduk, “Trajectory Design for Aircraft Taxi
Automation to Benefit Trajectory-Based Operations,” in Proc. of the 7th
Asian Control Conference, 2009, pp. 99 - 104.
[11] H. Lee, I. Simaiakis, and H. Balakrishnan, “A Comparison of Aircraft
Trajectory-Based and Aggregate Queue-Based Control of Airport Taxi
Processes,” in Proc. of the 29th Digital Avionics Systems Conference,
2010, pp. 1.B.3-1 - 1.B.3-15.
[12] J. Chen and P. Stewart, “Planning Aircraft Taxiing Trajectories via a
Multi-Objective Immune Optimisation” in Proc. of the Seventh
International Conference on Natural Computation (ICNC), 2011, pp.
2235 - 2240.
[13] S. Ravizza, J. Chen, J. D. Atkin, E. Burke, and P. Stewart, “The Trade-Off
between Taxi Time and Fuel Consumption in Airport Ground
Movement”, Public Transport, vol. 5, no. 1-2, pp. 25-40, 2013.
[14] M. Weiszer, J. Chen, S. Ravizza, J. Atkin, and P. Stewart, “A Heuristic
Approach to Greener Airport Ground Movement”, in Proc. of the 2014
IEEE Congress on Evolutionary Computation (CEC), 2014, pp.
3280-3286.
[15] S. Ravizza, J. Chen, J. A. D. Atkin, P. Stewart, and E. K. Burke, “Aircraft
Taxi Time Prediction: Comparisons and Insights”, Applied Soft
Computing, Part C, pp. 397-406, 2014.
[16] J. Kennedy and R. C. Eberhart, “Particle Swarm Optimization,” in Proc.
of the IEEE International Conference on Neural Networks, 1995, pp.
1942-1948.
[17] Y. Shi and R. Eberhart, “A Modified Particle Swarm Optimizer”, in Proc.
of the IEEE International Conference of Evolutionary Computation, 1998,
pp. 69-73.
[18] L. d. S. Coelho, “An Efficient Particle Swarm Approach for
Mixed-Integer Programming in Reliability–Redundancy Optimization
Applications”, Reliability Engineering & System Safety, vol. 94, no. 4, pp.
830-837, 2009.
[19] D. Zhou, X. Gao, G. Liu, C. Mei, D. Jiang, and Y. Liu, “Randomization in
Particle Swarm Optimization for Global Search Ability”, Expert Systems
with Applications, vol. 38, no. 12, pp. 15356-15364, 2011.
[20] T. Nikoleris, G. Gupta, and M. Kistler, “Detailed Estimation of Fuel
Consumption and Emissions during Aircraft Taxi Operations at Dallas/Fort worth International Airport”, Transportation Research Part
D: Transport and Environment, vol. 16, no. 4, pp. 302-308, 2011.
[1] SESAR Definition Phase 1: Deliverable 1, Eurocontrol, 2006.
[2] Advanced Surface Movement Guidance and Control Systems (A-SMGCS)
Manual, International Civil Aviation Organization, 2004.
[3] V. H. L. Cheng, “Research Progress on an Automation Concept for
Surface Operation with Time-Based Trajectories,” in Proc. of the
Integrated Communications, Navigation and Surveillance Conference,
2007, pp. 1 – 13.
[4] Á. G. Marín, “Airport Management: Taxi Planning,” Annals of
Operations Research, vol. 143, no. 1, pp. 191-202, 2006.
[5] J. A. Atkin, E. K. Burke, and S. Ravizza, “The Airport Ground Movement
Problem: Past and Current Research and Future Directions,” in Proc. of
the 4th International Conference on Research in Air Transportation
(ICRAT), 2010, pp. 131-138.
[6] J. W. Smeltink and M. J. Soomer, “An Optimisation Model for Airport
Taxi Scheduling,” in Proc. of INFORMS Annual Meeting, 2004.
[7] P. C. Roling and H. G. Visser, “Optimal Airport Surface Traffic Planning
Using Mixed-Integer Linear Programming,” International Journal of
Aerospace Engineering, vol. 2008, pp. 1-11, 2008.
[8] G. L. Clare and A. G. Richards, “Optimization of Taxiway Routing and
Runway Scheduling,” IEEE Trans. Intelligent Transportation Systems,
vol. 12, no. 4, pp. 1000-1013, 2011.
[9] Á. G. Marín, “Airport Taxi Planning: Lagrangian Decomposition,”
Journal of Advanced Transportation, vol. 47, no. 4, pp. 461-474, 2011.
[10] V. H. L. Cheng and G. D. Sweriduk, “Trajectory Design for Aircraft Taxi
Automation to Benefit Trajectory-Based Operations,” in Proc. of the 7th
Asian Control Conference, 2009, pp. 99 - 104.
[11] H. Lee, I. Simaiakis, and H. Balakrishnan, “A Comparison of Aircraft
Trajectory-Based and Aggregate Queue-Based Control of Airport Taxi
Processes,” in Proc. of the 29th Digital Avionics Systems Conference,
2010, pp. 1.B.3-1 - 1.B.3-15.
[12] J. Chen and P. Stewart, “Planning Aircraft Taxiing Trajectories via a
Multi-Objective Immune Optimisation” in Proc. of the Seventh
International Conference on Natural Computation (ICNC), 2011, pp.
2235 - 2240.
[13] S. Ravizza, J. Chen, J. D. Atkin, E. Burke, and P. Stewart, “The Trade-Off
between Taxi Time and Fuel Consumption in Airport Ground
Movement”, Public Transport, vol. 5, no. 1-2, pp. 25-40, 2013.
[14] M. Weiszer, J. Chen, S. Ravizza, J. Atkin, and P. Stewart, “A Heuristic
Approach to Greener Airport Ground Movement”, in Proc. of the 2014
IEEE Congress on Evolutionary Computation (CEC), 2014, pp.
3280-3286.
[15] S. Ravizza, J. Chen, J. A. D. Atkin, P. Stewart, and E. K. Burke, “Aircraft
Taxi Time Prediction: Comparisons and Insights”, Applied Soft
Computing, Part C, pp. 397-406, 2014.
[16] J. Kennedy and R. C. Eberhart, “Particle Swarm Optimization,” in Proc.
of the IEEE International Conference on Neural Networks, 1995, pp.
1942-1948.
[17] Y. Shi and R. Eberhart, “A Modified Particle Swarm Optimizer”, in Proc.
of the IEEE International Conference of Evolutionary Computation, 1998,
pp. 69-73.
[18] L. d. S. Coelho, “An Efficient Particle Swarm Approach for
Mixed-Integer Programming in Reliability–Redundancy Optimization
Applications”, Reliability Engineering & System Safety, vol. 94, no. 4, pp.
830-837, 2009.
[19] D. Zhou, X. Gao, G. Liu, C. Mei, D. Jiang, and Y. Liu, “Randomization in
Particle Swarm Optimization for Global Search Ability”, Expert Systems
with Applications, vol. 38, no. 12, pp. 15356-15364, 2011.
[20] T. Nikoleris, G. Gupta, and M. Kistler, “Detailed Estimation of Fuel
Consumption and Emissions during Aircraft Taxi Operations at Dallas/Fort worth International Airport”, Transportation Research Part
D: Transport and Environment, vol. 16, no. 4, pp. 302-308, 2011.
@article{"International Journal of Engineering, Mathematical and Physical Sciences:70863", author = "Zhang Tianci and Ding Meng and Zuo Hongfu and Zeng Lina and Sun Zejun", title = "A Two-Stage Airport Ground Movement Speed Profile Design Methodology Using Particle Swarm Optimization", abstract = "Automation of airport operations can greatly improve
ground movement efficiency. In this paper, we study the speed profile
design problem for advanced airport ground movement control and
guidance. The problem is constrained by the surface four-dimensional
trajectory generated in taxi planning. A decomposed approach of two
stages is presented to solve this problem efficiently. In the first stage,
speeds are allocated at control points, which ensure smooth speed
profiles can be found later. In the second stage, detailed speed profiles
of each taxi interval are generated according to the allocated control
point speeds with the objective of minimizing the overall fuel
consumption. We present a swarm intelligence based algorithm for the
first-stage problem and a discrete variable driven enumeration method
for the second-stage problem, since it only has a small set of discrete
variables. Experimental results demonstrate the presented
methodology performs well on real world speed profile design
problems.", keywords = "Airport ground movement, fuel consumption, particle
swarm optimization, smoothness, speed profile design.", volume = "9", number = "9", pages = "516-7", }
