Constraint-based local search for container stowage slot planning
- Dario Pacino,
- ,
- Tom Bebbington
Research Output:
Conference Article in Proceeding or Book/Report chapter
Article in proceedings
Peer-reviewPublication Information
Output type
Research Output:
Conference Article in Proceeding or Book/Report chapter
Article in proceedings
Peer-reviewHost publication Subtitle
Proceedings of the International MultiConference of Engineers and Computer ScientistsOriginal language
EnglishPublication milestones
- Published - 2012
Publication status
Published - 2012
Volume
1Publisher
IAENGISBN (Print)
978-988-19251-1-4Publication IDs
- Scopus: 84867443547
Host publication title
IMECS 2012Abstract
Due to the economical importance of stowage planning, there
recently has been an increasing interest in developing optimization
algorithms for this problem. We have developed 2-phase approach that
in most cases can generate near optimal stowage plans within a few
hundred seconds for large deep-sea vessels. This paper describes the
constrained-based local search algorithm used in the second phase of
this approach where individual containers are assigned to slots in
each bay section. The algorithm can solve this problem in an average of
0.18 seconds per bay, corresponding to a 20 seconds runtime for the
entire vessel. The algorithm has been validated on a benchmark suite
of 133 industrial instances for which 86% of the instances were
solved to optimality.
recently has been an increasing interest in developing optimization
algorithms for this problem. We have developed 2-phase approach that
in most cases can generate near optimal stowage plans within a few
hundred seconds for large deep-sea vessels. This paper describes the
constrained-based local search algorithm used in the second phase of
this approach where individual containers are assigned to slots in
each bay section. The algorithm can solve this problem in an average of
0.18 seconds per bay, corresponding to a 20 seconds runtime for the
entire vessel. The algorithm has been validated on a benchmark suite
of 133 industrial instances for which 86% of the instances were
solved to optimality.
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