Genetic Programming with Multi-tree Representation for Dynamic Flexible Job Shop Scheduling
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{Zhang:2018:AJCAImt,
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author = "Fangfang Zhang and Yi Mei and Mengjie Zhang",
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title = "Genetic Programming with Multi-tree Representation for
Dynamic Flexible Job Shop Scheduling",
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booktitle = "Australasian Joint Conference on Artificial
Intelligence",
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year = "2018",
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editor = "Tanja Mitrovic and Bing Xue and Xiaodong Li",
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volume = "11320",
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series = "LNCS",
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pages = "472--484",
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address = "Wellington, New Zealand",
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month = dec # " 11-14",
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publisher = "Springer",
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keywords = "genetic algorithms, genetic programming, Multi-tree
representation Flexible job shop scheduling Dynamic
changes",
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isbn13 = "978-3-030-03990-5",
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URL = "http://link.springer.com/chapter/10.1007/978-3-030-03991-2_43",
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DOI = "doi:10.1007/978-3-030-03991-2_43",
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abstract = "Flexible job shop scheduling (FJSS) can be regarded as
an optimization problem in production scheduling that
captures practical and challenging issues in real-world
scheduling tasks such as order picking in manufacturing
and cloud computing. Given a set of machines and jobs,
FJSS aims to determine which machine to process a
particular job (by routing rule) and which job will be
chosen to process next by a particular machine (by
sequencing rule). In addition, dynamic changes are
unavoidable in the real-world applications. These
features lead to difficulties in real-time scheduling.
Genetic programming (GP) is well-known for the
flexibility of its representation and tree-based GP is
widely and typically used to evolve priority functions
for different decisions. However, a key issue for the
tree-based representation is how it can capture both
the routing and sequencing rules simultaneously. To
address this issue, we proposed to use multi-tree GP
(MTGP) to evolve both routing and sequencing rules
together. In order to enhance the performance of MTGP
algorithm, a novel tree swapping crossover operator is
proposed and embedded into MTGP. The results suggest
that the multi-tree representation can achieve much
better performance with smaller rules and less training
time than cooperative co-evolution for GP in solving
dynamic FJSS problems. Furthermore, the proposed tree
swapping crossover operator can greatly improve the
performance of MTGP.",
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notes = "conf/ausai/ZhangMZ18",
- }
Genetic Programming entries for
Fangfang Zhang
Yi Mei
Mengjie Zhang
Citations