GLOP: Learning Global Partition and Local Construction for Solving Large-scale Routing Problems in Real-time
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866916330666459136 |
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| author | Ye, Haoran Wang, Jiarui Liang, Helan Cao, Zhiguang Li, Yong Li, Fanzhang |
| author_facet | Ye, Haoran Wang, Jiarui Liang, Helan Cao, Zhiguang Li, Yong Li, Fanzhang |
| contents | The recent end-to-end neural solvers have shown promise for small-scale routing problems but suffered from limited real-time scaling-up performance. This paper proposes GLOP (Global and Local Optimization Policies), a unified hierarchical framework that efficiently scales toward large-scale routing problems. GLOP partitions large routing problems into Travelling Salesman Problems (TSPs) and TSPs into Shortest Hamiltonian Path Problems. For the first time, we hybridize non-autoregressive neural heuristics for coarse-grained problem partitions and autoregressive neural heuristics for fine-grained route constructions, leveraging the scalability of the former and the meticulousness of the latter. Experimental results show that GLOP achieves competitive and state-of-the-art real-time performance on large-scale routing problems, including TSP, ATSP, CVRP, and PCTSP. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_08224 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | GLOP: Learning Global Partition and Local Construction for Solving Large-scale Routing Problems in Real-time Ye, Haoran Wang, Jiarui Liang, Helan Cao, Zhiguang Li, Yong Li, Fanzhang Artificial Intelligence Machine Learning The recent end-to-end neural solvers have shown promise for small-scale routing problems but suffered from limited real-time scaling-up performance. This paper proposes GLOP (Global and Local Optimization Policies), a unified hierarchical framework that efficiently scales toward large-scale routing problems. GLOP partitions large routing problems into Travelling Salesman Problems (TSPs) and TSPs into Shortest Hamiltonian Path Problems. For the first time, we hybridize non-autoregressive neural heuristics for coarse-grained problem partitions and autoregressive neural heuristics for fine-grained route constructions, leveraging the scalability of the former and the meticulousness of the latter. Experimental results show that GLOP achieves competitive and state-of-the-art real-time performance on large-scale routing problems, including TSP, ATSP, CVRP, and PCTSP. |
| title | GLOP: Learning Global Partition and Local Construction for Solving Large-scale Routing Problems in Real-time |
| topic | Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2312.08224 |