Food safety sampling inspections are critical for risk prevention in complex supply chains, yet the extremely low frequency of high-risk samples poses substantial challenges for accurate risk prediction. To address the limitations of conventional machine learning models under severe class imbalance, this study proposes a unified Bagging–Stacking framework that integrates stacking ensembles, bagging, and SMOTE–Tomek hybrid resampling to enhance minority-class detection in food safety risk prediction. The stacking ensemble serves as the core of the framework, combining five tree-based base learners with Logistic Regression as the meta-learner to enhance classification robustness. Balanced bootstrap subsets generated through bagging and SMOTE–Tomek hybrid resampling further improve minority-class representation, while a probability-based threshold optimization mechanism is incorporated to refine high-risk classification. Experiments on real-world inspection data show that the proposed framework substantially improves high-risk recall while simultaneously increasing precision, yielding the highest F1 among all compared models. It also maintains a stable overall performance across varying test set proportions, demonstrating strong robustness and consistent generalization under varying evaluation conditions. SHAP analysis identifies storage conditions, production month, shelf life, package, and food category as key contributors to risk prediction, aligning with established mechanisms of food safety risk formation. Overall, the proposed framework provides accurate, robust, and interpretable support for food safety risk prediction, offering practical value for proactive risk prevention and more efficient regulatory resource allocation.

Strategic planning for airport design and operations is a challenging task, since it should consider: (1) a variety of alternative futures; (2) their consequences for airport performance; and, (3) possible strategies for ensuring that the airport meets its business objectives. Existing models and tools for quantifying airport performance require significant data and substantial modeling effort, and do not readily produce the information relevant to decision-makers. Therefore, a fast and simple simulation model is needed that provides insight into the key performance indicators of an airport for different strategies across different futures. In this paper, a simple simulation model is developed. Different scenarios, with increasing traffic and changing traffic distribution, are simulated with a general simulation platform called ProModel, which can provide output data for airport capacity evaluation and checks for bottleneck. Case studies show that the model can be easily executed and proved to be feasible to quickly generate interest results for decision-makers.