Survey on Genetic Programming and Machine Learning Techniques for Heuristic Design in Job Shop Scheduling
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- @Article{Zhang:ieeeTEVC,
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author = "Fangfang Zhang and Yi Mei and Su Nguyen and
Mengjie Zhang",
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journal = "IEEE Transactions on Evolutionary Computation",
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title = "Survey on Genetic Programming and Machine Learning
Techniques for Heuristic Design in Job Shop
Scheduling",
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keywords = "genetic algorithms, genetic programming",
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ISSN = "1089-778X",
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DOI = "doi:10.1109/TEVC.2023.3255246",
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abstract = "Job shop scheduling is a process of optimising the use
of limited resources to improve the production
efficiency. Job shop scheduling has a wide range of
applications such as order picking in the warehouse and
vaccine delivery scheduling under a pandemic. In
real-world applications, the production environment is
often complex due to dynamic events such as job
arrivals over time and machine breakdown. Scheduling
heuristics, e.g., dispatching rules, have been
popularly used to prioritise the candidates such as
machines in manufacturing to make good schedules
efficiently. Genetic programming, has shown its
superiority in learning scheduling heuristics for job
shop scheduling automatically due to its flexible
representation. This survey firstly provides
comprehensive discussions of recent designs of genetic
programming algorithms on different types of job shop
scheduling. In addition, we notice that in the recent
years, a range of machine learning techniques such as
feature sele",
-
notes = "Also known as \cite{10065588}",
- }
Genetic Programming entries for
Fangfang Zhang
Yi Mei
Su Nguyen
Mengjie Zhang
Citations