Summary of some of the methods reviewed
| Research | Method | Dataset | Evaluation |
|---|---|---|---|
| Servos et al. (2019) | Support Vector Regression (SVR) | Real-world multi-dimensional container transportation relationship from Germany to the United States | Prediction accuracy with a mean absolute error of 17 h for delivery time up to 30 days |
| Erkmen et al. (2022) | Support Vector Machine (SVM) | Sample dataset obtained from Kaggle | Mean MAE achieved with the backward-looking approach (3.81) |
| Mahajan et al. (2022) | Multiple Regression Techniques | Internal data on shipments stored by a company, and secondly, maritime traffic data | 89% accuracy |
| Kandula et al. (2021) | Decision Support Framework | Two real-world datasets from a major e-commerce platform | 10.2% savings in delivery costs |
| Bani-Mustafa et al. (2018) | Fixed and Random Multivariate Regression Model | Three years of data from Elghanem Desert transportation (2014–2016) | Detection of 38.7% of all changes in delivery time |
| Alnahhal et al. (2021) | Linear Regression and Logistic Regression | Real data from a logistics company | Type 1 error with an average value of 0.07 |
| Barros et al. (2023) | Random Forest Model | Empirical data from a large automobile manufacturer | Average reduction of 18%–24% in mean absolute errors |
| Pineda-Jaramillo et al. (2023) | Tuned LightGBM | data from the Luxembourg National Railway Company | accuracy of 93.8% |
| Al-Saghir (2022) | Logistic Regression | Dataco supply chain dataset from Kaggle | accuracy of 75.13% |
| Shi et al. (2021) | combining eXtreme Gradient Boosting (XGBoost) and Bayesian optimization (BO | two high-speed railway lines in China | RMSE of 2.686/1.887 |
| Research | Method | Dataset | Evaluation |
|---|---|---|---|
| Support Vector Regression (SVR) | Real-world multi-dimensional container transportation relationship from Germany to the United States | Prediction accuracy with a mean absolute error of 17 h for delivery time up to 30 days | |
| Support Vector Machine (SVM) | Sample dataset obtained from Kaggle | Mean MAE achieved with the backward-looking approach (3.81) | |
| Multiple Regression Techniques | Internal data on shipments stored by a company, and secondly, maritime traffic data | 89% accuracy | |
| Decision Support Framework | Two real-world datasets from a major e-commerce platform | 10.2% savings in delivery costs | |
| Fixed and Random Multivariate Regression Model | Three years of data from Elghanem Desert transportation (2014–2016) | Detection of 38.7% of all changes in delivery time | |
| Linear Regression and Logistic Regression | Real data from a logistics company | Type 1 error with an average value of 0.07 | |
| Random Forest Model | Empirical data from a large automobile manufacturer | Average reduction of 18%–24% in mean absolute errors | |
| Tuned LightGBM | data from the Luxembourg National Railway Company | accuracy of 93.8% | |
| Logistic Regression | Dataco supply chain dataset from Kaggle | accuracy of 75.13% | |
| combining eXtreme Gradient Boosting (XGBoost) and Bayesian optimization (BO | two high-speed railway lines in China | RMSE of 2.686/1.887 |
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