Leveraging artificial intelligence in modern supply chains
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...
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| Format: | Final Year Project / Dissertation / Thesis |
| Published: |
2025
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| Online Access: | http://eprints.utar.edu.my/7012/1/fyp_IA_2025_NQY.pdf http://eprints.utar.edu.my/7012/ |
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| _version_ | 1854094457186549760 |
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| author | Ng, Qiao Ying |
| author_facet | Ng, Qiao Ying |
| author_sort | Ng, Qiao Ying |
| building | UTAR Library |
| collection | Institutional Repository |
| content_provider | Universiti Tunku Abdul Rahman |
| content_source | UTAR Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | 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. |
| format | Final Year Project / Dissertation / Thesis |
| id | my-utar-eprints.7012 |
| institution | Universiti Tunku Abdul Rahman |
| publishDate | 2025 |
| record_format | eprints |
| spelling | my-utar-eprints.70122025-12-28T14:24:26Z Leveraging artificial intelligence in modern supply chains Ng, Qiao Ying T Technology (General) 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. 2025-06 Final Year Project / Dissertation / Thesis NonPeerReviewed application/pdf http://eprints.utar.edu.my/7012/1/fyp_IA_2025_NQY.pdf Ng, Qiao Ying (2025) Leveraging artificial intelligence in modern supply chains. Final Year Project, UTAR. http://eprints.utar.edu.my/7012/ |
| spellingShingle | T Technology (General) Ng, Qiao Ying Leveraging artificial intelligence in modern supply chains |
| title | Leveraging artificial intelligence in modern supply chains |
| title_full | Leveraging artificial intelligence in modern supply chains |
| title_fullStr | Leveraging artificial intelligence in modern supply chains |
| title_full_unstemmed | Leveraging artificial intelligence in modern supply chains |
| title_short | Leveraging artificial intelligence in modern supply chains |
| title_sort | leveraging artificial intelligence in modern supply chains |
| topic | T Technology (General) |
| url | http://eprints.utar.edu.my/7012/1/fyp_IA_2025_NQY.pdf http://eprints.utar.edu.my/7012/ |
| url_provider | http://eprints.utar.edu.my |