Virgile Galle | Searcharxiv

arXiv2017

The Stochastic Container Relocation Problem

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.

cs.DS↗

arXiv2015

Container Relocation Problem: Approximation, Asymptotic, and Incomplete Information

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 respecting a given order of retrieval. While the problem is known to be NP-hard, certain algorithms such as the A* search and heuristics perform reasonably well on many instances of the problem. In this paper, we first focus on the A* search algorithm, and analyze lower and upper bounds that are easy to compute and can be used to prune nodes. Our analysis sheds light on which bounds result in fast computation within a given approximation gap. We present extensive simulation results that improve upon our theoretical analysis, and further show that our method finds the optimum solution on most instances of medium-size bays. On "hard" instances, our method finds an approximate solution with a small gap and within a time frame that is fast for practical applications. We also study the average-case asymptotic behavior of the CRP where the number of columns grows. We calculate the expected number of relocations in the limit, and show that the optimum number of relocations converges to a simple and intuitive lower-bound. We further study the CRP with incomplete information by relaxing the assumption that the order of retrieval of all containers are initially known. This assumption is particularly unrealistic in ports without an appointment system. We assume that the retrieval order of a subset of containers is known initially and the retrieval order of the remaining containers is observed later at a given specific time. Before this time, we assume a probabilistic distribution on the retrieval order of unknown containers. We combine the A* algorithm with sampling technique to solve this two-stage stochastic optimization problem. We show that our algorithm is fast and the error due to sampling and pruning is reasonably small.

cs.DS↗