The Stochastic Container Relocation Problem
Name
Galle_Stochastic_Container_Relocation_Full.pdf
Description
Main Article, updated September 2017
Size
1.08 MB
Format
Adobe PDF
Checksum (MD5)
f16f2ea443ad4ac5b9627ac07952c7b5
Author(s)
Galle, V.
Borjian Boroujeni, S.
Manshadi, V.H.
Barnhart, C.
Jaillet, P.
Date Issued
March 28, 2017
Series/Report no.
MIT Sloan School Working Paper;5193-17
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.
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