Abstract
With ever-growing numbers of passengers and complexity of the air transport system, it becomes more and more of a challenge to manage the system in an effective, safe, and resilient manner. This is especially evident when disruptions occur. Understanding and improving resilience of the air transport system and its adaptive capacity to disruptions is essential for the system’s uninterrupted successful performance. Using theoretical findings from behavioral sciences, this paper makes the first steps towards formalization of the adaptive capacity of resilience of the air transport system with a particular focus on its ability to anticipate. To this end, an expressive logic-based language called Temporal Trace Language is used. The proposed approach is illustrated by a case study, in which anticipatory mechanisms are implemented in an agent-based airport terminal operations model, to deal with a disruptive scenario of unplanned and challenging passenger demand at the security checkpoint. Results showed that the timing of an adaptive action could have a significant influence on reducing the risk of saturation of the system, where saturation implies performance loss. Additionally, trade-off relations were obtained between cost, corresponding to the extra resources mobilized, and the benefits, such as a decrease in risk of saturation of the passenger queue.
Highlights
The modern air transport system is a complex, highly dynamic, sociotechnical system with many diverse actors actively interacting with each other
In the second type of anticipation, predictions concern only future payoffs, and not future states. This form of anticipation is too limited for our purposes, as decisions in the air transport system are often based on prediction of future states
Farjadian et al (2017) presented one of the first attempts to formalize and operationalize theoretical resilience concepts stemming from behavioral sciences in the context of a shared pilot-autopilot control architecture
Summary
The modern air transport system is a complex, highly dynamic, sociotechnical system with many diverse actors actively interacting with each other. This research presents an illustrative case study, which implements anticipatory abilities in an agent-based airport terminal operations model, to deal with a disruptive scenario of unplanned and challenging passenger demand at the security checkpoint. This form of anticipation is too limited for our purposes, as decisions in the air transport system are often based on prediction of future states (e.g., the accumulation of passengers in a queue over time).
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