Learning Strategies on Scheduling Heuristics of Genetic Programming in Dynamic Flexible Job Shop Scheduling
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InProceedings{Zhang:2022:CEC,
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author = "Fangfang Zhang and Yi Mei and Su Nguyen and
Mengjie Zhang",
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booktitle = "2022 IEEE Congress on Evolutionary Computation (CEC)",
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title = "Learning Strategies on Scheduling Heuristics of
Genetic Programming in Dynamic Flexible Job Shop
Scheduling",
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year = "2022",
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editor = "Carlos A. Coello Coello and Sanaz Mostaghim",
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address = "Padua, Italy",
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month = "18-23 " # jul,
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isbn13 = "978-1-6654-6708-7",
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abstract = "Dynamic flexible job shop scheduling is an important
combinatorial optimisation problem that covers valuable
practical applications such as order picking in
warehouses and service allocation in cloud computing.
Machine assignment and operation sequencing are two key
decisions to be considered simultaneously in dynamic
flexible job shop scheduling. Genetic programming has
been successfully and widely used to learn scheduling
heuristics, including a routing rule for machine
assignment and a sequencing rule for operation
sequencing simultaneously. There are mainly two types
of learning strategies to evolve scheduling heuristics,
i.e., learning one rule by fixing the other rule, and
learning the routing rule and the sequencing rule
simultaneously. However, there is no guidance on which
learning strategy to use in specific cases. To fill
this gap, this paper provides a comprehensive study of
learning strategies on scheduling heuristics of genetic
programming in dynamic flexible job shop scheduling by
comparing five learning strategies, including two
strategies that are extended from the existing studies.
The results show that learning two rules
simultaneously, either using cooperative coevolution or
multi-tree representation, is more effective than only
learning one type of rule. Cooperative coevolution is
recommended if an algorithm aims to handle a problem by
dividing it into small sub-problems, and focuses on the
characteristics of routing rule and sequencing rule.
Genetic programming with multi-tree representation that
treats the routing rule and the sequencing rule as an
individual, is preferred to reduce the complexities of
algorithms.",
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keywords = "genetic algorithms, genetic programming, Sequential
analysis, Job shop scheduling, Processor scheduling,
Heuristic algorithms, Dynamic scheduling, Routing,
Surrogate, Instance Rotation, Brood Recombination,
Dynamic Job Shop Scheduling",
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DOI = "doi:10.1109/CEC55065.2022.9870243",
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notes = "Also known as \cite{9870243}",
- }
Genetic Programming entries for
Fangfang Zhang
Yi Mei
Su Nguyen
Mengjie Zhang
Citations