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

Abstract

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

Suez Canal: An Exploratory Study on Alternative Trade Routes

“…According to Rodrigue (2020), transport is the act of transferring a load, using some transportation modality (air, waterway or land), from one place to another. The air mode plays a crucial role in transporting high-value goods (Sahoo, Pasayat, Bhowmick, Fernandes, & Tiwari, 2022). At the same time, perishable, time-sensitive, or items needed quickly in SC are often sent by air too, despite this being an expensive mode of transportation.…”

Intelligent algorithms applied to the prediction of air freight transportation delays

“…The effect of a delay at a node in the supply chain could further propagate and disrupt other nodes (Sahoo et al, 2022). Thus, three models for controlling propagation delays and enhancing resilience in intermodal logistics networks have also been proposed when providing a quantitative method for managing schedule coordination plans (Chen et al, 2016).…”

“…The effect of a delay at a node in the supply chain could further propagate and disrupt other nodes (Sahoo et al. , 2022).…”

An Optimized Bagging Ensemble Learning Approach Using BESTrees for Predicting Students’ Performance

“…The ultimate aim of the ensemble method is to lessen the possibility of choosing underperforming single classifier while advancing the outcome of a single algorithm by combining several individual algorithms into an ensemble model [13]. Building ensemble classifiers for high-dimensional and large-dataset problems is tremendously beneficial; finding an individual classifier in a single step is impossible due to the problem's scale and intricacy [7].…”

“…In recent years, a notable amount of research introduced new bagging variants to balance the missing properties of the prior ones. For example, in the study of Sahoo et al [8], they proposed a roadmap to unravel common issues in the cargo shipping sector by forecasting the delay and by giving freight forwarders an advantage over their competitors. Furthermore, they bared that merging predictions attained by single classifiers into bagging ensemble improves overall accuracy while reducing error.…”