Abstract:

This study presents a predictive discrete event simulation model designed to forecast operation times in container terminals. Utilizing a diverse set of algorithms, including Linear Regression, Decision Tree, Random Forest, Support Vector Machine (SVM), XGBoost, CatBoost, and Artificial Neural Networks (ANN), the model aims to enhance accuracy in delivery date prediction for container tracking. The dataset from Kaggle, which contains historical tracking and operational data, serves as the basis for training and evaluating these algorithms. By comparing the performance of traditional machine learning techniques with advanced ensemble methods and deep learning approaches, the study provides insights into the effectiveness of various predictive models in optimizing container terminal operations. The findings offer valuable contributions to the field of logistics and supply chain management, aiding in the efficient planning and execution of terminal operations.

Keywords: Predictive Discrete Event Simulation, Container Terminal Operations, Linear Regression, Decision Tree, Random Forest, SVM, XGBoost, CatBoost,Delivery Date Prediction.

This study presents a predictive discrete event simulation model designed to forecast operation times in container terminals. Utilizing a diverse set of algorithms, including Linear Regression, Decision Tree, Random Forest, Support Vector Machine (SVM), XGBoost, CatBoost, and Artificial Neural Networks (ANN), the model aims to enhance accuracy in delivery date prediction for container tracking. The dataset from Kaggle, which contains historical tracking and operational data, serves as the basis for training and evaluating these algorithms. By comparing the performance of traditional machine learning techniques with advanced ensemble methods and deep learning approaches, the study provides insights into the effectiveness of various predictive models in optimizing container terminal operations. The findings offer valuable contributions to the field of logistics and supply chain management, aiding in the efficient planning and execution of terminal operations.

Keywords: Predictive Discrete Event Simulation, Container Terminal Operations, Linear Regression, Decision Tree, Random Forest, SVM, XGBoost, CatBoost,Delivery Date Prediction.