Skip to main content
Log in

Airline Base Schedule Optimisation by Flight Network Annealing

  • Published:
Annals of Operations Research Aims and scope Submit manuscript

Abstract

A system for rigorous airline base schedule optimisation is described. The architecture of the system reflects the underlying problem structure. The architecture is hierarchical consisting of a master problem for logical aircraft schedule optimisation and a sub-problem for schedule evaluation.

The sub-problem is made up of a number of component sub-problems including connection generation, passenger choice modelling, passenger traffic allocation by simulation and revenue and cost determination.

Schedule optimisation is carried out by means of simulated annealing of flight networks. The operators for the simulated annealing process are feasibility preserving and form a complete set of operators.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. E.H.L. Aarts and J. Korst, Simulated Annealing and Boltzmann Machines (Wiley, 1989).

  2. C.J. Adkins, Equilibrium Thermodynamics (Cambridge University Press, 1989).

  3. M.B. Akiva and S.R Lerman, Discrete Choice Analysis: Theory and Application to Travel Demand, (MIT Press, 1985) p. 45.

  4. L. Bodin, B.L. Golden, A. Assad andM. Ball, Routing and scheduling of vehicles and crews: the state of the art, Computers and Operations Research 10 (1983).

  5. W.-C. Chiang and R.A. Russel, Simulated annealing metaheuristics for the vehicle routing problem with time windows, Annals of Operations Research 63 (1996) 3–27.

    Google Scholar 

  6. W.D. Cook, G.C. Shaw and D.M.Wallace, Measuring the quality of an airline schedule: A case study, Canadian Aeronautics and Space Journal 33 (1987) 26–30.

    Google Scholar 

  7. M.M. Etschmaier and D.F.X. Mathaisel, Airline scheduling: an overview, Transportation Science 19(2) (1985) 127–138.

    Google Scholar 

  8. T.A. Feo and J.F. Bard, Flight scheduling and maintenance base planning, Management Science 35(12) (1989) 1415–1432.

    Google Scholar 

  9. B. Gidas, Nonstationary Markov chains and convergence of the annealing algorithm, Journal of Statistical Physics 39(1/2) (1985) 73–131.

    Google Scholar 

  10. Z. Gu, E.L. Johnson, G.L. Nemhauser and Y. Wang, Some properties of the fleet assignment problem, Operations Research Letters 15 (1994) 59–71.

    Google Scholar 

  11. K. Kastella, Aircraft route optimization using adaptive simulated annealing, in: Proc. of the 1991 IEEE National Aerospace and Electronics Conference NAECON, New York (1991) pp. 1123-1129.

  12. J.G. Kemeny and J.L. Snell, Finite Markov Chains (Springer, New York, 1976).

    Google Scholar 

  13. J.J. Langerman and E.M. Ehlers, Agent based airline scheduling, Computers and Industrial Engineering 33(3-4) (1997) 849–852.

    Google Scholar 

  14. C.-Y. Lee, L. Lei and M. Pinedo, Current trends in deterministic scheduling, Annals of Operations Research 70 (1997) 1–41.

    Google Scholar 

  15. T. Li and J.S. Mashford, A parallel genetic algorithm for quadratic assignment, in: Proc ISMM International Conference on Parallel and Distributed Computing and Systems, New York (1990) pp. 391-394.

  16. S.L. Martins, P.M. Pardalos, M.G.C. Resende and C.C. Ribeiro, Greedy randomized adaptive search procedures for the Steiner problem in graphs, in: Randomisation Methods in Algorithm Design, eds. P. Pardalos, S. Rajasekaran and J. Rolim, DIMACS Series in Discrete Mathematics and Theoretical Computer Science, Vol. 43 (American Mathematical Society, 1997) pp. 133-145.

  17. J.S. Mashford, A method for the solution of fixed charge problems by Benders' decomposition, Optimization 21 (1990) 101–107.

    Google Scholar 

  18. D.F.X. Mathaisel, Decision support for airline schedule planning, Journal of Combinatorial Optimization 1 (1997) 251–275.

    Google Scholar 

  19. N. Metropolis, A. Rosenbluth, M. Rosenbluth, A. Teller and E. Teller, Equations of state calculations by fast computing machines, J. Chem. Phys. 21 (1953) 1087–1091.

    Google Scholar 

  20. D.M. Ryan and J.C. Falkner, On the integer properties of scheduling set partitioning models, European Journal of Operational Research 35 (1988) 442–446.

    Google Scholar 

  21. E.M. Smith, Airlines maintenance model, in: Proc. Summer Computer Simulation Conference, Vol. 2 (June 1970) pp. 1125–1133.

    Google Scholar 

  22. R. Subramanian, R.P. Scheff Jr, J.D. Quillinan, D.S. Wiper and R.E. Marsten, Coldstart: fleet assignment at Delta Air Lines, Interfaces 24(1) (1994) 104–120.

    Google Scholar 

  23. S.R. Thangiah and P. Petrovic, Introduction to genetic heuristics and vehicle routing problems with complex constraints, in: Advances in Computational and Stochastic Optimization, Logic Programming and Heuristic Search, Interfaces in Computer Science and Operations Research, ed. D.L. Woodruff (Kluwer Academic, 1998) pp. 253-286.

  24. S. Voss, S. Martello, I.H. Osman and C. Roucairol, eds., Meta-Heuristics, Advances and Trends in Local Search Paradigms for Optimization (Kluwer Academic, 1999).

  25. Z. Zhu, The aircraft rotation problem, Ph.D. thesis, Georgia Institute of Technology (1994).

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

About this article

Cite this article

Mashford, J., Marksjö, B. Airline Base Schedule Optimisation by Flight Network Annealing. Annals of Operations Research 108, 293–313 (2001). https://doi.org/10.1023/A:1016027516013

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1023/A:1016027516013

Keywords

Navigation