Survey on Genetic Programming and Machine Learning Techniques for Heuristic Design in Job Shop Scheduling

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@Article{Zhang:ieeeTEVC,

• author = "Fangfang Zhang and Yi Mei and Su Nguyen and Mengjie Zhang",
• journal = "IEEE Transactions on Evolutionary Computation",
• title = "Survey on Genetic Programming and Machine Learning Techniques for Heuristic Design in Job Shop Scheduling",
• keywords = "genetic algorithms, genetic programming",
• ISSN = "1089-778X",
• DOI = "doi:10.1109/TEVC.2023.3255246",
• 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

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