Crossover Operators Between Multiple Scheduling Heuristics with Genetic Programming for Dynamic Flexible Job Shop Scheduling
Created by W.Langdon from
gp-bibliography.bib Revision:1.7975
- @InProceedings{zhu:2024:CEC,
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author = "Luyao Zhu and Fangfang Zhang and Mengyuan Feng and
Ke Chen2 and Xiaodong Zhu and Mengjie Zhang",
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title = "Crossover Operators Between Multiple Scheduling
Heuristics with Genetic Programming for Dynamic
Flexible Job Shop Scheduling",
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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, Sequential
analysis, Job shop scheduling, Processor scheduling,
Sociology, Production, Dynamic scheduling, dynamic
flexible job shop scheduling, crossover operators,
scheduling heuristics",
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isbn13 = "979-8-3503-0837-2",
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DOI = "doi:10.1109/CEC60901.2024.10612212",
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abstract = "Dynamic flexible job shop scheduling (DFJSS) is an
important combinatorial optimisation problem that aims
to optimise machine resources to improve production
efficiency. Multi-tree genetic programming (MTGP) has
been widely used to learn the routing rule and the
sequencing rule for DFJSS simultaneously. Unlike
traditional genetic programming that only operates
crossover on a single tree, MTGP has various cases to
conduct crossover since a genetic programming
individual consists of more than one tree. Different
crossover operators may affect the performance of MTGP
for DFJSS. However, the investigation into different
crossover operators in MTGP for DFJSS is rare.
Specifically, it is not clear what influence will have
on MTGP if involving both the routing rule and the
sequencing rule for crossover. To this end, this paper
provides a comprehensive investigation of four possible
crossover cases between multiple scheduling heuristics
with MTGP for DFJSS. The four operators are designed
according to the number of trees/rules that crossover
operator works on, and whether swapping full trees
between parents. The results show that although the
compared algorithms have comparable results in most
scenarios, MTGP with both rules for crossover and the
swapping strategy is ranked as the best one. Further
analyses show that the sizes of learnt rules are highly
related to the crossover operators, and crossover
involving more rules can increase the rule sizes, and
vice versa. In addition, the population diversity and
the number of unique features in the learnt rules of
MTGP with both rules for crossover are increased to
learn effective rules.",
-
notes = "also known as \cite{10612212}
WCCI 2024",
- }
Genetic Programming entries for
Luyao Zhu
Fangfang Zhang
Mengyuan Feng
Ke Chen2
Xiaodong Zhu
Mengjie Zhang
Citations