随机集装箱重定位问题
The Stochastic Container Relocation Problem
摘要：
集装箱重定位问题（CRP）关注的是找到一个集装箱的移动序列，最大限度地减少重新定位所需的数量，以检索所有集装箱，同时尊重给定的检索顺序。然而，在实际操作中，知道容器的完整检索顺序的假设特别不现实。本文研究了放松这一假设的随机CRP（SCRP）。一个新的多阶段的随机模型，称为批量模型，介绍，动机，并与现有的模型（在线模型）进行比较。两个主要的贡献是一个最佳的算法，称为修剪最好的第一次搜索（PBFS）和随机近似算法称为PBFS近似有界的平均误差。这两种算法，适用于批量和在线模型，是基于一个新的家庭的下限，我们展示了一些理论性质。此外，我们引入了两个新的性能优于现有的最好的ecologistics。算法、界限和启发式算法在广泛的计算部分进行了测试。最后，强有力的计算证据的基础上，我们推测的最优性的“调平”启发式在一个特殊的“没有信息”的情况下，在任何检索阶段，任何剩余的容器是同样有可能被检索下一个。
英文摘要：
The Container Relocation Problem (CRP) is concerned with finding a sequence of moves of containers that minimizes the number of relocations needed to retrieve all containers, while respecting a given order of retrieval. However, the assumption of knowing the full retrieval order of containers is particularly unrealistic in real operations. This paper studies the stochastic CRP (SCRP), which relaxes this assumption. A new multi-stage stochastic model, called the batch model, is introduced, motivated, and compared with an existing model (the online model). The two main contributions are an optimal algorithm called Pruning-Best-First-Search (PBFS) and a randomized approximate algorithm called PBFS-Approximate with a bounded average error. Both algorithms, applicable in the batch and online models, are based on a new family of lower bounds for which we show some theoretical properties. Moreover, we introduce two new heuristics outperforming the best existing heuristics. Algorithms, bounds and heuristics are tested in an extensive computational section. Finally, based on strong computational evidence, we conjecture the optimality of the "Leveling" heuristic in a special "no information" case, where at any retrieval stage, any of the remaining containers is equally likely to be retrieved next.
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