Robust Aircraft Routing

Abstract

We propose a robust optimization approach to minimize total propagated delay in the aircraft routing problem, a setting first developed by Lan et al. (2006) and then extended by Dunbar et al. (2012, 2014). Instead of minimizing the expected total propagated delay by assuming that flight leg delays follow specific probability distributions, our model minimizes the maximal possible total propagated delay when flight leg delays lie in a pre-specified uncertainty set. We develop exact and tractable solution approaches for our robust model. The major contribution of our model is that it allows us to explicitly model and handle correlation in flight leg delays (e.g., due to weather or various air traffic management initiatives) that existing approaches cannot efficiently incorporate. Using both historical delay data and simulated data, we evaluate the performance of our model and benchmark against the state-of-the-research stochastic approach (Dunbar et al. 2014). In most of the cases, we observe that our model outperforms the existing approach in lowering the mean, reducing volatility, and mitigating extreme values of total propagated delay. In the cases where a deficit in one of the three criteria exists, gains in the other two criteria usually offset this disadvantage. These results suggest that robust optimization approaches can provide promising results for the aircraft routing problem.

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“…Aircraft routing is also known as aircraft rotation, aircraft assignment, or tail assignment (Bazargan 2010), (Dong et al 2016), (Kenan et al 2018). For further references on aircraft routing, readers may refer to (Gürkan et al 2016), (Marla et al 2018), (Safaei and Jardine 2018), (Yan and Kung 2018), and (Parmentier and Meunier 2020).…”

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