Designing Relocation Rules with Genetic Programming for the Online Container Relocation Problem
Created by W.Langdon from
gp-bibliography.bib Revision:1.8010
- @InProceedings{durasevic:2024:CEC2,
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author = "Marko Durasevic and Mateja Dumic and
Francisco Javier Gil-Gala",
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title = "Designing Relocation Rules with Genetic Programming
for the Online Container Relocation Problem",
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booktitle = "2024 IEEE Congress on Evolutionary Computation (CEC)",
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year = "2024",
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editor = "Bing Xue",
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address = "Yokohama, Japan",
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month = "30 " # jun # " - 5 " # jul,
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publisher = "IEEE",
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keywords = "genetic algorithms, genetic programming,
Metaheuristics, Evolutionary computation, Containers,
online container relocation problem, relocation rules",
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isbn13 = "979-8-3503-0837-2",
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DOI = "doi:10.1109/CEC60901.2024.10611835",
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abstract = "The container relocation problem (CRP) in shipping
terminals is becoming increasingly important due to the
growing amount of transferred goods. Until now, the
most commonly investigated problem variant has been the
offline CRP, in which the order in which the containers
need to be retrieved is known beforehand. However, in
many real-world situations this is not the case, which
is modelled using the online CRP variant. In this
variant, not all information is available from the
beginning, but rather, it becomes available as the
problem is being solved. Unfortunately, many
traditional metaheuristic solution methods can not be
applied to such a problem variant, which prompts the
application of problem-specific heuristics called
relocation rules (RRs). However, RRs are challenging to
design manually, which prompted the application of
genetic programming (GP) to design them automatically.
Since GP was used only to design RRs for the offline
problem variant, we apply GP to design relocation rules
for the online variant in this study. The performance
of GP is investigated under different levels of
information availability to measure its performance.
The results demonstrate that GP can evolve RRs that
perform better than existing manually designed ones.
Furthermore, the results show that in certain cases,
rules generated for one level of information
availability perform well for other levels,
demonstrating that the evolved rules exhibit a good
generalisation capability.",
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notes = "also known as \cite{10611835}
WCCI 2024",
- }
Genetic Programming entries for
Marko Durasevic
Mateja Dumic
Francisco Javier Gil Gala
Citations