Leveraging artificial intelligence in modern supply chains - Ng, Qiao Ying (2025)
This project addresses the challenge of last-mile delivery delays caused by urban traffic congestion by creating a smart route optimization system that combines the traffic prediction with classical pathfinding. A synthetic dataset was generated to simulate urban traffic flows, and a Long Short-Term Memory (LSTM) model was trained to forecast short-term congestion patterns. These predictions were converted into congestion factors and applied as dynamic weights within Dijkstra’s algorithm to compute adaptive delivery routes. A Streamlit-based dashboard was designed to visualize model performance, predicted traffic conditions, optimized routes, and system-level evaluations in a simulated real-time environment. Evaluation results demonstrated that the LSTM model achieved reliable short-term forecasts, outperforming a baseline by more than 25% in error reduction, while the congestion-aware routing consistently avoided heavily congested edges. The prototype validates the feasibility of combining predictive analytics with graph-based optimization, offering a practical foundation for enhancing efficiency and reliability in last-mile logistics operations.