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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2025
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| Accès en ligne: | https://arxiv.org/abs/2504.09046 |
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| _version_ | 1866917991783858176 |
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| author | Wang, Ruoqi Li, Jiawei |
| author_facet | Wang, Ruoqi Li, Jiawei |
| contents | In container terminal yards, the Container Rehandling Problem (CRP) involves rearranging containers between stacks under specific operational rules, and it is a pivotal optimization challenge in intelligent container scheduling systems. Existing CRP studies primarily focus on minimizing reallocation costs using two-dimensional bay structures, considering factors such as container size, weight, arrival sequences, and retrieval priorities. This paper introduces an enhanced deepening search algorithm integrated with improved lower bounds to boost search efficiency. To further reduce the search space, we design mutually consistent pruning rules to avoid excessive computational overhead. The proposed algorithm is validated on three widely used benchmark datasets for the Unrestricted Container Rehandling Problem (UCRP). Experimental results demonstrate that our approach outperforms state-of-the-art exact algorithms in solving the more general UCRP variant, particularly exhibiting superior efficiency when handling containers within the same priority group under strict time constraints. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_09046 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | An Enhanced Iterative Deepening Search Algorithm for the Unrestricted Container Rehandling Problem Wang, Ruoqi Li, Jiawei Artificial Intelligence In container terminal yards, the Container Rehandling Problem (CRP) involves rearranging containers between stacks under specific operational rules, and it is a pivotal optimization challenge in intelligent container scheduling systems. Existing CRP studies primarily focus on minimizing reallocation costs using two-dimensional bay structures, considering factors such as container size, weight, arrival sequences, and retrieval priorities. This paper introduces an enhanced deepening search algorithm integrated with improved lower bounds to boost search efficiency. To further reduce the search space, we design mutually consistent pruning rules to avoid excessive computational overhead. The proposed algorithm is validated on three widely used benchmark datasets for the Unrestricted Container Rehandling Problem (UCRP). Experimental results demonstrate that our approach outperforms state-of-the-art exact algorithms in solving the more general UCRP variant, particularly exhibiting superior efficiency when handling containers within the same priority group under strict time constraints. |
| title | An Enhanced Iterative Deepening Search Algorithm for the Unrestricted Container Rehandling Problem |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2504.09046 |