Using simulation to estimate critical paths and survival functions in aircraft turnaround processes

Abstract

In the context of aircraft turnaround processes, this paper illustrates how simulation can be used not only to analyze critical activities and paths, but also to generate the associated survival functions-thus providing the probabilities that the turnaround can be completed before a series of target times. After motivating the relevance of the topic for both airlines and airports, the paper reviews some related work and proposes the use of Monte Carlo simulation to obtain the critical paths of the turnaround process and generate the associated survival function. This analysis is performed assuming stochastic completion times for each activity in the process-which contrast with current practices in which deterministic times are usually assumed. A series of numerical experiments considering the Boeing 737-800 aircraft are carried out. Different levels of passengers' occupancy are analyzed, as well as two alternative designs for the turnaround stage.

Simulation-based education involving online and on-campus models in different European universities

“…to be assigned during the check-in and transportation stages. Similar analysis can be performed on the security-control point, the boarding process (Mas et al 2013;Carmona et al 2014), the aircraft turn-around process (Silverio et al 2013;San Antonio et al 2017), the aircraft evacuation process (Estany et al 2017), etc.…”

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“…Another study illustrated how simulation could be used to analyse critical aircraft turnaround processes at airports (San Antonio et al 2017). A series of numerical experiments considering the Boeing 737-800 aircraft was presented.…”

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“…This uncertainty is another important challenge to be overcome (Schmidt et al., 2016), but it is barely considered in the handling optimization literature, nor in airport ground operations in general (Brownlee et al., 2018; Ng et al., 2018). Most research studies use deterministic values to model the operations' processing time (Al Bazi et al., 2016; Antonio et al., 2017), and only a small number of ground handling optimization studies address the unpredictable environment encountered in airports. Deterministic times are typically an optimistic estimate, so vehicle schedules are highly optimized but more likely to fail due to disturbances.…”