Asynchronous Cooperative Optimization of a Capacitated Vehicle Routing Problem Solution
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866915636397998080 |
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| author | Accorsi, Luca Laganà, Demetrio Michelotto, Federico Musmanno, Roberto Vigo, Daniele |
| author_facet | Accorsi, Luca Laganà, Demetrio Michelotto, Federico Musmanno, Roberto Vigo, Daniele |
| contents | We propose a parallel shared-memory schema to cooperatively optimize the solution of a Capacitated Vehicle Routing Problem instance with minimal synchronization effort and without the need for an explicit decomposition. To this end, we design FILO2$^x$ as a single-trajectory parallel adaptation of the FILO2 algorithm originally proposed for extremely large-scale instances and described in Accorsi and Vigo (2024). Using the locality of the FILO2 optimization applications, in FILO2$^x$ several possibly unrelated solution areas are concurrently asynchronously optimized. The overall search trajectory emerges as an iteration-based parallelism obtained by the simultaneous optimization of the same underlying solution performed by several solvers. Despite the high efficiency exhibited by the single-threaded FILO2 algorithm, the computational results show that, by better exploiting the available computing resources, FILO2$^x$ can greatly enhance the resolution time compared to the original approach, still maintaining a similar final solution quality for instances ranging from hundreds to hundreds of thousands customers. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_19445 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Asynchronous Cooperative Optimization of a Capacitated Vehicle Routing Problem Solution Accorsi, Luca Laganà, Demetrio Michelotto, Federico Musmanno, Roberto Vigo, Daniele Distributed, Parallel, and Cluster Computing Discrete Mathematics We propose a parallel shared-memory schema to cooperatively optimize the solution of a Capacitated Vehicle Routing Problem instance with minimal synchronization effort and without the need for an explicit decomposition. To this end, we design FILO2$^x$ as a single-trajectory parallel adaptation of the FILO2 algorithm originally proposed for extremely large-scale instances and described in Accorsi and Vigo (2024). Using the locality of the FILO2 optimization applications, in FILO2$^x$ several possibly unrelated solution areas are concurrently asynchronously optimized. The overall search trajectory emerges as an iteration-based parallelism obtained by the simultaneous optimization of the same underlying solution performed by several solvers. Despite the high efficiency exhibited by the single-threaded FILO2 algorithm, the computational results show that, by better exploiting the available computing resources, FILO2$^x$ can greatly enhance the resolution time compared to the original approach, still maintaining a similar final solution quality for instances ranging from hundreds to hundreds of thousands customers. |
| title | Asynchronous Cooperative Optimization of a Capacitated Vehicle Routing Problem Solution |
| topic | Distributed, Parallel, and Cluster Computing Discrete Mathematics |
| url | https://arxiv.org/abs/2511.19445 |