Advertisement

Optimized Air Routes Connections for Real Hub Schedule Using SMPSO Algorithm

  • H. RahilEmail author
  • B. Abou El Majd
  • M. Bouchoum
Chapter
Part of the Operations Research/Computer Science Interfaces Series book series (ORCS, volume 62)

Abstract

The choice to open new routes for air carriers, airports and regional governments have some tools to promote desirable connections to be offered toward specific destinations. With a given flight program, the air carrier decision to open new routes faces several constraints and affects the flight schedules in a remarkable way. This work is distinguished by the fact of being the first to introduce the problem of connectivity in the network of an airline whose main activity is based on air hub structure, optimizing the insertion of new airline routes while ensuring the best fill rate seats and avoiding significant delays during correspondence. Quality of Service Index (QSI) will be considered as a duel parameter for the profit of a new opened market. This aspect of decision making is formulated as multi-objective problem by testing the impact of a new insertion in term of delays, generated with related costs and financial gain, and the quality of service offered to a target customers. The SMPSO Algorithm is adopted to generate a Pareto-optimal front composed of many optimal departure times toward the new opening insuring the best filling ratio with minimum connecting times. Experiences are based on real instance of Royal Air Maroc flights schedule on the hub of Casablanca .

Keywords

Route networks Hub and spokes Outbound/Inbound connection Flight schedule Multi-objective optimization SMPSO algorithm Pareto optimal 

References

  1. 1.
    R. Abeyratne, Achieving competitive advantage through connectivity and innovation: an application in airline hubbing, in Competition and Investment in Air Transport (Springer International Publishing, Cham, 2016), pp. 131–143Google Scholar
  2. 2.
    G. Burghouwt, Airline Network Development in Europe and Its Implications for Airport Planning (Ashgate, Aldershot, 2007)Google Scholar
  3. 3.
    G. Burghouwt, R. Redondi, Connectivity in air transport networks: an assessment of models and applications. J. Transp. Econ. Policy 47, 35–53 (2013)Google Scholar
  4. 4.
    K. Deb, D. Kalyanmoy, Multi-Objective Optimization Using Evolutionary Algorithms (Wiley, New York, 2001)Google Scholar
  5. 5.
    N. Dennis, Competition between hub airports in Europe and a methodology for forecasting connecting traffic, in World Transport Research: Selected Proceedings of the 8th World Conference on Transport Research, vol. 1, 1999Google Scholar
  6. 6.
    C.V. Dyke, M. Meketon, B.W. Patty, in Network Analysis and Simulation, ed. by B.W. Patty. Handbook of Operations Research Applications at Railroads (Springer, New York, 2015), pp. 191–217Google Scholar
  7. 7.
    N. Ghaffari-Nasab, M. Ghazanfari, E. Teimoury, Robust optimization approach to the design of hub-and-spoke networks. Int. J. Adv. Manuf. Technol. 76, 1091–1110 (2014)CrossRefGoogle Scholar
  8. 8.
    Global Airport Connectivity Monitor, IATA Aviation Information and Research Department (2000)Google Scholar
  9. 9.
    D. Guimarans, P. Arias, M.M. Mota, in Large Neighbourhood Search and Simulation for Disruption Management in the Airline Industry, ed. by M.M. Mota, I.F.D.L. Mota, D.G. Serrano. Applied Simulation and Optimization (Springer International Publishing, Cham, 2015), pp. 169–201Google Scholar
  10. 10.
    R. Guimera, S. Mossa, A. Turtschi, L.A.N. Amaral, The worldwide air transportation network: anomalous centrality, community structure, and cities’ global roles. Proc. Natl. Acad. Sci. 102, 7794–7799 (2005)CrossRefGoogle Scholar
  11. 11.
    D.-W.-I.L. Ionescu, D.C. Gwiggner, P.D.N. Kliewer, Data analysis of delays in airline networks. Bus. Inf. Syst. Eng. 1–15 (2015)Google Scholar
  12. 12.
    J. Kennedy, in Particle Swarm Optimization, ed. by C. Sammut, G.I. Webb. Encyclopedia of Machine Learning (Springer, New York, 2011), pp. 760–766Google Scholar
  13. 13.
    P. Malighetti, G. Martini, S. Paleari, R. Redondi, The Efficiency of European Airports: Do the Importance in the EU Network and the Intensity of Competition Matter? University of Bergamo, Bergamo, 2008Google Scholar
  14. 14.
    H. Rahil, B. Abou El Majd, A multi-objective optimization approach for hub connections. Application to the insertion of new flights in airline schedule, in The 5th International Conference on Metaheuristics and Nature Inspired Computing, META’14, Marrakech, 2014Google Scholar
  15. 15.
    H. Rahil, B. Abou El Majd, M. Bouchoum, Optimization of inserting a new flight in airline schedule using evolutionary algorithms, in WISTL’14 Workshop Proceeding, 2015Google Scholar
  16. 16.
    S.A. Taher, A. Karimian, M. Hasani, A new method for optimal location and sizing of capacitors in distorted distribution networks using PSO algorithm. Simul. Model. Pract. Theory 19, 662–672 (2011)CrossRefGoogle Scholar
  17. 17.
    J. Veldhuis, The competitive position of airline networks. J. Air Transp. Manage. 3, 181–188 (1997)CrossRefGoogle Scholar
  18. 18.
    M.D. Wittman, W.S. Swelbar, Modeling Changes in Connectivity at U.S. Airports: A Small Community Perspective. Report No. ICAT-2013-05 (2013)Google Scholar
  19. 19.
    C. Zhang, H. Shao, Y. Li, Particle swarm optimisation for evolving artificial neural network, in 2000 IEEE International Conference on Systems, Man, and Cybernetics, vol. 4 (2000), pp. 2487–2490Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Laboratory of Computer Science and Decision SupportFaculty of SciencesCasablancaMorocco

Personalised recommendations