A hybrid ensemble learning-based prediction model to minimise delay in air cargo transport using bagging and stacking

Manufacturing productivity is inextricably linked to air freight handling for the global delivery of finished and semi-finished goods. In this article, our focus capture transport risk associated with which difference between actual planned time arrival a shipment. To mitigate time-related uncertainties, it essential predict delays adequate precision. Initially, data from case study in transportation logistics sector were pre-processed divided into categories based on duration various legs. Existing datasets are transformed series features, followed by extracting important features using decision tree-based algorithm. delay maximum accuracy, we used an improved hybrid ensemble learning-based prediction model bagging stacking enabled characteristics like time, flight schedule, We also calculated dependency accuracy point during business process execution examined while predicting. Our results show all predictive methods consistently have precision at least 70 per cent, provided lead-time half process. Consistently, proposed provides strategic sustainable insights decision-makers cargo handling.