Abstract
In a scenario characterized by a continuous growth of air transportation demand, the runways of large airports serve hundreds of aircraft every day. Aircraft sequencing is a challenging problem that aims to increase runway capacity in order to reduce delays as well as the workload of air traffic controllers. In many cases, the air traffic controllers solve the problem using the simple “first-come-first-serve” (FCFS) rule. In this paper, we present a rolling horizon approach which partitions a sequence of aircraft into chunks and solves the aircraft sequencing problem (ASP) individually for each of these chunks. Some rules for deciding how to partition a given aircraft sequence are proposed and their effects on solution quality investigated. Moreover, two mixed integer linear programming models for the ASP are reviewed in order to formalize the problem, and a tabu search heuristic is proposed for finding solutions to the ASP in a short computation time. Finally, we develop an IRHA which, using different chunking rules, is able to find solutions significantly improving on the FCFS rule for real-world air traffic instances from Milano Linate Airport.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Abela, J., Abramson, D., Krishnamoorthy, M., De Silva, A., & Mills, G. (1993). Computing optimal schedules for landing aircraft (pp. 71–90). Adelaide, Australia: Proceeding 12th National ASOR Conference.
Atkin, J. A. D., Burke, E. K., Greenwood, J. S., & Reeson, D. (2007). Hybrid metaheuristic to aid runway scheduling at London Heathrow airport. Transportation Science, 41, 90–106.
Atkin, J. A. D., Burke, E. K., Greenwood, J. S., & Reeson, D. (2007). On-line decision support for take-off runway scheduling under uncertain taxi time at London Heathrow airport. Journal of Scheduling, 11, 323–346.
Atkin, J. A. D., Burke, E. K., Greenwood, J. S., & Reeson, D. (2008). A metaheuristic approach to aircraft departure scheduling at London Heathrow airport. Computer-aided Systems in Public Transport. Lecture Notes in Economics and Mathematical Systems, vol. 600, pp. 235–252.
Atkin, J. A. D., Burke, E. K., Greenwood, J. S., & Reeson, D. (2009). An examination of take-off scheduling constraints at London Heathrow airport. Public Transport, 1(3), 169–187.
Balakrishnan, H., & Chandran, B. G. (2010). Algorithms for scheduling runway operations under constrained position shifting. Operations Research, 58(6), 1650–1665.
Beasley, J. E., Krishnamoorthy, M., Sharaiha, Y. M., & Abramson, D. (2000). Scheduling aircraft landing-the static case. Transportation Science, 34, 180–197.
Beasley, J. E., Krishnamoorthy, M., Sharaiha, Y. M., & Abramson, D. (2004). Displacement problem and dynamically scheduling aircraft landings. Journal of the Operational Research Society, 55, 54–64.
Beasley, J. E., & Pinol, H. (2006). Scatter search and bionomic algorithms for the aircraft landing problem. European Journal of Operational Research, 127(2), 439–462.
Bennel, J. A., Mesgarpour, M., & Potts, C. N. (2011). Airport runway scheduling. A Quarterly Journal of Operations Research, 4OR(9), 115–138.
Burke, E. K., De Causmaecker, P., De Maere, G., Mulder, J., & Paelinck, M. (2010). A multi-objective approach for robust airline scheduling. Computers and Operations Research, 37(5), 822–832.
Cheng, V. H. L., Crawford, L. S., & Menon, P. K. (1999). Air traffic control using genetic search techniques. In Proceedings of the IEEE International Conference on Control Applications.
Ciesielski, V., & Scerri, P. (1998). Real time genetic scheduling of aircraft landing times. In Proceeding of the 1998 IEEE International Conference on Evolutionary Computation (ICEC98). New York, NY: IEEE, pp. 360–364.
Ernst, A. T., Krishnamoorthy, M., & Storer, R. H. (1999). Heuristic and exact algorithms for scheduling aircraft landings. Networks, 34, 229–241.
Furini, F., Kidd, M. P., Persiani, C. A., & Toth, P. (2014). State space reduced dynamic programming for the aircraft sequencing problem with constrained position shifting. Lecture notes in computer science, pp. 267–279.
Furini, F., Persiani, C. A., & Toth, P. (2012). Aircraft sequencing problems via a rolling horizon algorithm. Lecture Notes in Computer Science, Vol. 7422, pp. 273–284.
IBM ILOG, (2012) CPLEX Optimizer 12: CPLEX http://www-01.ibm.com/software/commerce/optimization/cplex-optimizer/.
Kimms, A. (1997). Production and logistics: Multi-level lot sizing and scheduling. Rolling Planning Horizon 239–245.
Persiani, C. A., & Bagassi, S. (2011). Integration of AMAN and DMAN using Particle Swarm Optimization. In Electronic Proceedings of the 3rd CEAS AirSpace Conference.
Samà, M., D’Ariano, A., & Pacciarelli, D. (2013). Rolling horizon approach for aircraft scheduling in the terminal control area of busy airports. Procedia Social and Behavioral Sciences, 80, 531–552.
Stevens, G. (1995). An approach to scheduling aircraft landing times using genetic algorithms. Dissertation Thesis. Melbourne: Department of Computer Science, RMIT University.
Wang, B., Xi, Y., & Gu, H. (2005). Terminal penalty rolling scheduling based on an initial schedule for single-machine scheduling problem. Computers and Operations Research, 32(11), 3059–3072.
Acknowledgments
The authors would like to thank the anonymous referees, whose comments greatly improved the presentation of the paper.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Furini, F., Kidd, M.P., Persiani, C.A. et al. Improved rolling horizon approaches to the aircraft sequencing problem. J Sched 18, 435–447 (2015). https://doi.org/10.1007/s10951-014-0415-8
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10951-014-0415-8