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The Stochastic Container Relocation Problem

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Show simple item record Galle, V. Borjian Boroujeni, S. Manshadi, V.H. Barnhart, C. Jaillet, P. 2017-03-29T01:12:06Z 2017-03-29T01:12:06Z 2017-03-28
dc.description.abstract 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. en_US
dc.language.iso en_US en_US
dc.relation.ispartofseries MIT Sloan School Working Paper;5193-17
dc.rights Attribution-NonCommercial-NoDerivs 3.0 United States *
dc.rights.uri *
dc.title The Stochastic Container Relocation Problem en_US
dc.type Working Paper en_US

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Attribution-NonCommercial-NoDerivs 3.0 United States Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 United States