Large Neighbourhood Search and simulation for disruption management in the airline industry

Daniel Guimarans, Pol Arias, Miguel Mujica Mota

Research output: Chapter in Book/Report/Conference proceedingChapter (Book)Researchpeer-review


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.
Original languageEnglish
Title of host publicationApplied Simulation and Optimization
Subtitle of host publicationIn Logistics, Industrial and Aeronautical Practice
EditorsMiguel Mujica Mota, Idalia Flores De La Mota, Daniel Guimarans Serrano
Place of PublicationCham Switzerland
Number of pages33
ISBN (Electronic)9783319150321, 9783319150338
ISBN (Print)9783319150321
Publication statusPublished - 2015

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