Research on Time Constraint Strategy of Flight Ground Support Operations Based on Causal Inference

To improve the punctuality of flight schedules, causal inference methods are introduced to model the potential causal structure and intervention effects among ground support operations of flights. The effectiveness of these methods in improving flight punctuality is verified under experimental conditions. When the causal relationship of Flight Ground Support (FGS) is determined, the research initiates from the perspective of FGS. A time-constrained strategy based on the Q-learning causal optimal strategy algorithm is proposed to transform causal effects into causal strategies. Initially, the influencing factors of FGS operations are classified into intervention groups. The causal effects of these influencing factors on their target support operations are calculated, and the influence degrees of the causes on the results within the causal relationship are investigated. Subsequently, the time constraint of the FGS process is characterized as a Markov decision process. The experimental results indicate that, compared with the traditional probability strategy, the causal strategy that considers the causal relationship enables over 51% of the flight plans to depart on time, with an average increase of 2.79%. The proposed method is not restricted to a specific airport or a single ground handling process configuration. Under the condition that ground handling operations are observable and sufficient historical operational data are available, it provides an interpretable optimization framework for time-constraint decision-making in flight ground handling operations across airports of different scales.