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Large Neighbourhood Search and Simulation for Disruption Management in the Airline Industry

In: Applied Simulation and Optimization

Author

Listed:
  • Daniel Guimarans

    (National ICT Australia (NICTA), Optimisation Research Group)

  • Pol Arias

    (Internet Interdisciplinary Institute (IN3-UOC), Smart Logistics and Production Group)

  • Miguel Mujica Mota

    (Amsterdam University of Applied Sciences, Aviation Academy)

Abstract

The airline industry is one of the most affected by operational disruptions, defined as deviations from originally planned operations. Due to airlines network configuration, delays are rapidly propagated to connecting flights, substantially increasing unexpected costs for the airlines. The goal in these situations is therefore to minimise the impact of the disruption, reducing delays and the number of affected flights, crews and passengers. In this chapter, we describe a methodology that tackles the Stochastic Aircraft Recovery Problem, which considers the stochastic nature of air transportation systems. We define an optimisation approach based on the Large Neighbourhood Search metaheuristic, combined with simulation at different stages in order to ensure solutions’ robustness. We test our approach on a set of instances with different characteristics, including some instances originating from real data provided by a Spanish airline. In all cases, our approach performs better than a deterministic approach when system’s variability is considered.

Suggested Citation

  • Daniel Guimarans & Pol Arias & Miguel Mujica Mota, 2015. "Large Neighbourhood Search and Simulation for Disruption Management in the Airline Industry," Springer Books, in: Miguel Mujica Mota & Idalia Flores De La Mota & Daniel Guimarans Serrano (ed.), Applied Simulation and Optimization, edition 127, pages 169-201, Springer.
    • Handle: RePEc:spr:sprchp:978-3-319-15033-8_6
    DOI: 10.1007/978-3-319-15033-8_6
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