Bottleneck alleviation and scheduling optimization of flexible manufacturing system based on information-energy flow model

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@Article{Ju:2024:swevo,

• author = "Zeliang Ju and Yan Wang and Zhen Quan and Xiang Liu and Zhicheng Ji",
• title = "Bottleneck alleviation and scheduling optimization of flexible manufacturing system based on information-energy flow model",
• journal = "Swarm and Evolutionary Computation",
• year = "2024",
• volume = "89",
• pages = "101600",
• keywords = "genetic algorithms, genetic programming, Flexible job shop scheduling, Generation hyper-heuristics, Bottleneck identification, Bottleneck alleviation, Information flow model",
• ISSN = "2210-6502",
• URL = "https://www.sciencedirect.com/science/article/pii/S221065022400138X",
• DOI = "doi:10.1016/j.swevo.2024.101600",
• abstract = "The identification and alleviation of bottleneck machines in manufacturing systems are of paramount importance for optimising production decision-making in enterprises. Extensive research has been conducted by numerous scholars on the identification of bottleneck machines; however, there is a relative scarcity of research, particularly in the context of flexible manufacturing systems, regarding how to effectively alleviate the identified bottlenecks. In this paper, a novel hyper-heuristic evolutionary scheduling algorithm with a bottleneck alleviation strategy based on the information-energy flow model (HESA-BA-IEF) is proposed for training dispatching rules and optimising decision-making in flexible job shops with bottleneck machines. HESA-BA-IEF constructs an information-energy flow model for flexible job shops based on complex networks. Through this model, dynamic changes in machine processing status and energy consumption during the scheduling process can be effectively simulated, facilitating the accurate identification of bottleneck machines. Moreover, the algorithm adopts the genetic programming hyper-heuristics and introduces a scheduling mechanism considering the alleviation of bottleneck machines to optimise the makespan and total energy consumption, while simultaneously alleviating bottlenecks. Experiments show that HESA-BA-IEF generates dispatching rules that optimise scheduling and alleviate bottlenecks simultaneously. Furthermore, statistical analysis reveals a 7.71percent reduction in bottleneck metrics of production line due to 30.40percent scheduling rounds with bottleneck alleviation mechanism in effect",

}

Genetic Programming entries for Zeliang Ju Yan Wang Zhen Quan Xiang Liu Zhicheng Ji

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