A comparative study of machine learning approaches for large-scale vehicle routing
With the surge in demand for delivery services, accelerated by the COVID-19 pandemic, and the proliferation of data, Vehicle Routing Problems (VRP) have grown in size and complexity. Despite proven effective solvers for smaller-scale problems, addressing large-scale VRPs remains a challenge. T...
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| Other Authors: | |
| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2024
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| Online Access: | https://hdl.handle.net/10356/175066 |
| _version_ | 1826129349056659456 |
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| author | Chan, Ray |
| author2 | Zhang Jie |
| author_facet | Zhang Jie Chan, Ray |
| author_sort | Chan, Ray |
| collection | NTU |
| description | With the surge in demand for delivery services, accelerated by the COVID-19 pandemic, and the
proliferation of data, Vehicle Routing Problems (VRP) have grown in size and complexity. Despite
proven effective solvers for smaller-scale problems, addressing large-scale VRPs remains a
challenge. This project delves into the analysis, comparison, and exploration of two novel
approaches aimed specifically at tackling Large-scale Vehicle Routing Problems. Aiming to
provide insights into their effectiveness, efficiency, and applicability in real-world scenarios. We
investigate the performance of the Neural Combinatorial Optimization (NCO) model and compare
it with methods like Lin-Kernighan Heuristic (LKH3) and Learning to Delegate. Our analysis
indicates that the NCO model consistently outperforms the benchmark algorithm LKH3 and
Learning to Delegate for smaller datasets but exhibits diminishing performance for larger datasets,
suggesting scalability limitations. Furthermore, NCO demonstrates stronger performance on
Learning to Delegate datasets compared to its own, suggesting robust generalization capabilities. |
| first_indexed | 2024-10-01T07:39:07Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/175066 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T07:39:07Z |
| publishDate | 2024 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1750662024-04-19T15:45:45Z A comparative study of machine learning approaches for large-scale vehicle routing Chan, Ray Zhang Jie School of Computer Science and Engineering ZhangJ@ntu.edu.sg Computer and Information Science Large-scale vehicle routing With the surge in demand for delivery services, accelerated by the COVID-19 pandemic, and the proliferation of data, Vehicle Routing Problems (VRP) have grown in size and complexity. Despite proven effective solvers for smaller-scale problems, addressing large-scale VRPs remains a challenge. This project delves into the analysis, comparison, and exploration of two novel approaches aimed specifically at tackling Large-scale Vehicle Routing Problems. Aiming to provide insights into their effectiveness, efficiency, and applicability in real-world scenarios. We investigate the performance of the Neural Combinatorial Optimization (NCO) model and compare it with methods like Lin-Kernighan Heuristic (LKH3) and Learning to Delegate. Our analysis indicates that the NCO model consistently outperforms the benchmark algorithm LKH3 and Learning to Delegate for smaller datasets but exhibits diminishing performance for larger datasets, suggesting scalability limitations. Furthermore, NCO demonstrates stronger performance on Learning to Delegate datasets compared to its own, suggesting robust generalization capabilities. Bachelor's degree 2024-04-19T02:34:02Z 2024-04-19T02:34:02Z 2024 Final Year Project (FYP) Chan, R. (2024). A comparative study of machine learning approaches for large-scale vehicle routing. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/175066 https://hdl.handle.net/10356/175066 en SCSE23-0175 application/pdf Nanyang Technological University |
| spellingShingle | Computer and Information Science Large-scale vehicle routing Chan, Ray A comparative study of machine learning approaches for large-scale vehicle routing |
| title | A comparative study of machine learning approaches for large-scale vehicle routing |
| title_full | A comparative study of machine learning approaches for large-scale vehicle routing |
| title_fullStr | A comparative study of machine learning approaches for large-scale vehicle routing |
| title_full_unstemmed | A comparative study of machine learning approaches for large-scale vehicle routing |
| title_short | A comparative study of machine learning approaches for large-scale vehicle routing |
| title_sort | comparative study of machine learning approaches for large scale vehicle routing |
| topic | Computer and Information Science Large-scale vehicle routing |
| url | https://hdl.handle.net/10356/175066 |
| work_keys_str_mv | AT chanray acomparativestudyofmachinelearningapproachesforlargescalevehiclerouting AT chanray comparativestudyofmachinelearningapproachesforlargescalevehiclerouting |