A Hybrid Evolutionary Hyper-Heuristic Approach for Intercell Scheduling Considering Transportation Capacity
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @Article{Li:2016:ieeeASE,
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author = "Dongni Li and Rongxin Zhan and Dan Zheng and
Miao Li and Ikou Kaku",
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journal = "IEEE Transactions on Automation Science and
Engineering",
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title = "A Hybrid Evolutionary Hyper-Heuristic Approach for
Intercell Scheduling Considering Transportation
Capacity",
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year = "2016",
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volume = "13",
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number = "2",
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pages = "1072--1089",
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abstract = "The problem of intercell scheduling considering
transportation capacity with the objective of
minimizing total weighted tardiness is addressed in
this paper, which in nature is the coordination of
production and transportation. Since it is a practical
decision-making problem with high complexity and large
problem instances, a hybrid evolutionary
hyper-heuristic (HEH) approach, which combines
heuristic generation and heuristic selection, is
developed in this paper. In order to increase the
diversity and effectiveness of heuristic rules, genetic
programming is used to automatically generate new rules
based on the attributes of parts, machines, and
vehicles. The new rules are added to the candidate rule
set, and a rule selection genetic algorithm is
developed to choose appropriate rules for machines and
vehicles. Finally, scheduling solutions are obtained
using the selected rules. A comparative evaluation is
conducted, with some state-of-the-art hyper-heuristic
approaches which lack some of the strategies proposed
in HEH, with a meta-heuristic approach that is suitable
for large scale scheduling problems, and with
adaptations of some well-known heuristic rules.
Computational results show that the new rules generated
in HEH have similarities to the best-performing
human-made rules, but are more effective due to the
evolutionary processes in HEH. Moreover, the HEH
approach has advantages over other approaches in both
computational efficiency and solution quality, and is
especially suitable for problems with large instance
sizes.",
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keywords = "genetic algorithms, genetic programming, Job shop
scheduling, Processor scheduling, Search problems,
Vehicles, Discrete event systems, manufacturing
automation, scheduling, transportation",
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DOI = "doi:10.1109/TASE.2015.2470080",
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ISSN = "1545-5955",
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month = apr,
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notes = "Also known as \cite{7270346}",
- }
Genetic Programming entries for
Dongni Li
Rongxin Zhan
Dan Zheng
Miao Li
Ikou Kaku
Citations