A comprehensive analysis using Neural Networks, Random Forest, and XGBoost on simulation-based datasets for queue prediction and optimization.
This research project investigates the application of machine learning techniques to predict performance metrics in G/G/s queue systems. By analyzing simulation-generated datasets, the study compares the effectiveness of Neural Networks, Random Forest, and XGBoost algorithms in forecasting queue behavior and system performance.
The project demonstrates how modern machine learning approaches can provide accurate predictions for complex queue systems, offering insights into system optimization and resource allocation strategies.
Multi-layer perceptron implementation with backpropagation for queue prediction, featuring adaptive learning rates and dropout regularization.
Ensemble method using multiple decision trees with bootstrap sampling and feature importance ranking for robust predictions.
Gradient boosting implementation with regularization parameters and early stopping to prevent overfitting.
The project analyzes performance metrics from two distinct queue system simulations:
Queue with general arrival and service time distributions
Multi-server queue system with parallel processing capabilities
Comparative analysis of prediction accuracy across Neural Networks, Random Forest, and XGBoost models.
Queue waiting time (Wq) prediction with error analysis.
Aashrith Raj Tatipamula
King's University College
King's Internship Program
Machine Learning Research