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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2507.10397 |
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| _version_ | 1866912490244276224 |
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| author | Gouvêa, Alessandra M. M. M. Paulos, Nuno Uchoa, Eduardo Nascimento, Mariá C. V. |
| author_facet | Gouvêa, Alessandra M. M. M. Paulos, Nuno Uchoa, Eduardo Nascimento, Mariá C. V. |
| contents | This paper seeks to advance CVRP research by addressing the challenge of understanding the nuanced relationships between instance characteristics and metaheuristic (MH) performance. We present Instance Space Analysis (ISA) as a valuable tool that allows for a new perspective on the field. By combining the ISA methodology with a dataset from the DIMACS 12th Implementation Challenge on Vehicle Routing, our research enabled the identification of 23 relevant instance characteristics. Our use of the PRELIM, SIFTED, and PILOT stages, which employ dimensionality reduction and machine learning methods, allowed us to create a two-dimensional projection of the instance space to understand how the structure of instances affect the behavior of MHs. A key contribution of our work is that we provide a projection matrix, which makes it straightforward to incorporate new instances into this analysis and allows for a new method for instance analysis in the CVRP field. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_10397 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Instance space analysis of the capacitated vehicle routing problem Gouvêa, Alessandra M. M. M. Paulos, Nuno Uchoa, Eduardo Nascimento, Mariá C. V. Artificial Intelligence This paper seeks to advance CVRP research by addressing the challenge of understanding the nuanced relationships between instance characteristics and metaheuristic (MH) performance. We present Instance Space Analysis (ISA) as a valuable tool that allows for a new perspective on the field. By combining the ISA methodology with a dataset from the DIMACS 12th Implementation Challenge on Vehicle Routing, our research enabled the identification of 23 relevant instance characteristics. Our use of the PRELIM, SIFTED, and PILOT stages, which employ dimensionality reduction and machine learning methods, allowed us to create a two-dimensional projection of the instance space to understand how the structure of instances affect the behavior of MHs. A key contribution of our work is that we provide a projection matrix, which makes it straightforward to incorporate new instances into this analysis and allows for a new method for instance analysis in the CVRP field. |
| title | Instance space analysis of the capacitated vehicle routing problem |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2507.10397 |