改进深度优先搜索算法，高效解决集装箱重排难题。
An Enhanced Iterative Deepening Search Algorithm for the Unrestricted Container Rehandling Problem
- 结合优化下界与互洽剪枝规则，缩小搜索空间。
- 在三大基准数据集上优于现有最优算法，尤其在时间紧时表现更优。
- 适合智能码头调度系统，对高并发重排场景有实用价值。
在集装箱码头堆场中，集装箱重排问题（CRP）涉及在特定操作规则下重新排列堆叠中的集装箱，是智能调度系统中的核心优化挑战。现有研究主要聚焦于二维贝叶结构下最小化重排成本，考虑集装箱尺寸、重量、到达序列和取箱优先级等因素。本文提出一种增强型深度优先搜索算法，融合改进的下界估计，并设计互洽的剪枝规则以减少计算开销。该方法在三个广泛使用的无约束重排问题（UCRP）基准数据集上进行了验证。实验结果表明，所提方法在求解更具普适性的UCRP变体时，优于当前最先进的精确算法，尤其在严格时间约束下处理同一优先级组集装箱时展现出更优效率。
原文摘要 · Abstract (English)
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.
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