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Applied Simulation and Optimization pp 169–201Cite as

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

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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.

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Acknowledgments

NICTA is funded by the Australian Government through the Department of Communications and the Australian Research Council through the ICT Centre of Excellence Program.

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Authors and Affiliations

  1. Optimisation Research Group, National ICT Australia (NICTA), Sydney, Australia

    Daniel Guimarans

  2. Smart Logistics and Production Group, Internet Interdisciplinary Institute (IN3-UOC), Barcelona, Spain

    Pol Arias

  3. Aviation Academy, Amsterdam University of Applied Sciences, Amsterdam, Netherlands

    Miguel Mujica Mota

Authors
  1. Daniel Guimarans
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  2. Pol Arias
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  3. Miguel Mujica Mota
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Corresponding author

Correspondence to Daniel Guimarans .

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Editors and Affiliations

  1. Aviation Academy, Amsterdam University of Applied Sciences, Amsterdam, Noord-Holland, The Netherlands

    Miguel Mujica Mota

  2. Facultad de Ingeniería, Universidad Nacional Autónoma de México, Mexico City, Mexico

    Idalia Flores De La Mota

  3. Optimisation Research Group, NICTA, Sydney, New South Wales, Australia

    Daniel Guimarans Serrano

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Guimarans, D., Arias, P., Mota, M.M. (2015). Large Neighbourhood Search and Simulation for Disruption Management in the Airline Industry. In: Mujica Mota, M., De La Mota, I., Guimarans Serrano, D. (eds) Applied Simulation and Optimization. Springer, Cham. https://doi.org/10.1007/978-3-319-15033-8_6

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