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The Stochastic Container Relocation Problem
Name
1703.04769.pdf
Description
Submitted version
Size
1.16 MB
Format
Adobe PDF
Checksum (MD5)
6f7dbcb29c87b52adb5f178d7c10db85
Author(s)
Galle, V
Manshadi, VH
Boroujeni, S Borjian
Barnhart, C
Jaillet, P
Date Issued
2018
Journal
Transportation Science
Publisher
Institute for Operations Research and the Management Sciences (INFORMS)
Version
Original manuscript
Abstract
© 2018 INFORMS. 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.
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
10.1287/TRSC.2018.0828
