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

Chapter

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.

Keywords

Constraint Programming Constraint Satisfaction Problem Greedy Randomise Adaptive Search Procedure Total Delay Large Neighbourhood 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

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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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Optimisation Research GroupNational ICT Australia (NICTA)SydneyAustralia
  2. 2.Smart Logistics and Production GroupInternet Interdisciplinary Institute (IN3-UOC)BarcelonaSpain
  3. 3.Aviation AcademyAmsterdam University of Applied SciencesAmsterdamNetherlands

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