Resource Constrained Pathfinding with Enhanced Bidirectional A* Search
Fuente:
arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866915244746473472 |
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| author | Ahmadi, Saman Raith, Andrea Tack, Guido Jalili, Mahdi |
| author_facet | Ahmadi, Saman Raith, Andrea Tack, Guido Jalili, Mahdi |
| contents | The classic Resource Constrained Shortest Path (RCSP) problem aims to find a cost optimal path between a pair of nodes in a network such that the resources used in the path are within a given limit. Having been studied for over a decade, RCSP has seen recent solutions that utilize heuristic-guided search to solve the constrained problem faster. Building upon the bidirectional A* search paradigm, this research introduces a novel constrained search framework that uses efficient pruning strategies to allow for accelerated and effective RCSP search in large-scale networks. Results show that, compared to the state of the art, our enhanced framework can significantly reduce the constrained search time, achieving speed-ups of over to two orders of magnitude. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_13888 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Resource Constrained Pathfinding with Enhanced Bidirectional A* Search Ahmadi, Saman Raith, Andrea Tack, Guido Jalili, Mahdi Artificial Intelligence The classic Resource Constrained Shortest Path (RCSP) problem aims to find a cost optimal path between a pair of nodes in a network such that the resources used in the path are within a given limit. Having been studied for over a decade, RCSP has seen recent solutions that utilize heuristic-guided search to solve the constrained problem faster. Building upon the bidirectional A* search paradigm, this research introduces a novel constrained search framework that uses efficient pruning strategies to allow for accelerated and effective RCSP search in large-scale networks. Results show that, compared to the state of the art, our enhanced framework can significantly reduce the constrained search time, achieving speed-ups of over to two orders of magnitude. |
| title | Resource Constrained Pathfinding with Enhanced Bidirectional A* Search |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2412.13888 |