DOI: 10.1016/j.aei.2026.104353">
 

Data-driven modelling of unloading hours using explainable gradient boosting models

Document Type

Article

Publication Date

2026

Department/School

Engineering Technology

Publication Title

Advanced Engineering Informatics

Abstract

Unloading processes denote the extraction of finished goods and raw materials from transport units and their subsequent conveyance to designated locations. The efficiency of unloading processes is vital in supply chain and logistics management, regarded as an essential component. Delays in unloading operations result in numerous challenges, including heightened operational expenses, diminished labour efficiency, and supply chain bottlenecks. Consequently, it is essential to ascertain unloading times beforehand to mitigate these challenges, resulting in diminished idle time, enhanced overall efficiency, and optimized scheduling. Therefore, precise prediction of unloading times is critically significant. The novelty of this study lies in the application of machine learning techniques to improve operational efficiency by accurately predicting unloading time. To that end, this study employed LightGBM and XGBoost to predict the unloading time in a real case. The unloading time can be predicted with R2 score greater than 0.99 utilizing both models. Subsequently, the SHapley Additive exPlanations (SHAP) methodology was used to ascertain how each input feature contributed to the model’s output. The load of leg significantly influences the unloading time more than the gross weight of truck and the leg distance.

Comments

C. Cakiroglu is a faculty member in EMU's School of Engineering.

Link to Published Version

DOI: 10.1016/j.aei.2026.104353

Link to Full Text

EMU Library Accessibility Request

Share

COinS