The Stochastic Container Relocation Problem
研究了在不知道全部取箱顺序的随机环境下，如何通过新提出的批处理模型和两种算法（PBFS及其近似版）最小化集装箱重定位次数，对港口运营优化有参考价值。
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, which relaxes this assumption. A new multistage 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. The online appendix is available at https://doi.org/10.1287/trsc.2018.0828 .